diff --git a/.gitignore b/.gitignore index b95f1730e..9f0a11c21 100644 --- a/.gitignore +++ b/.gitignore @@ -26,3 +26,6 @@ TAGS # Files created by or for asv (airspeed velocity) .asv/ + +# Files created by Spyder +.spyproject/ diff --git a/benchmarks/flatsys_bench.py b/benchmarks/flatsys_bench.py new file mode 100644 index 000000000..0c0a5e53a --- /dev/null +++ b/benchmarks/flatsys_bench.py @@ -0,0 +1,107 @@ +# flatsys_bench.py - benchmarks for flat systems package +# RMM, 2 Mar 2021 +# +# This benchmark tests the timing for the flat system module +# (control.flatsys) and is intended to be used for helping tune the +# performance of the functions used for optimization-based control. + +import numpy as np +import math +import control as ct +import control.flatsys as flat +import control.optimal as opt + +# Vehicle steering dynamics +def vehicle_update(t, x, u, params): + # Get the parameters for the model + l = params.get('wheelbase', 3.) # vehicle wheelbase + phimax = params.get('maxsteer', 0.5) # max steering angle (rad) + + # Saturate the steering input (use min/max instead of clip for speed) + phi = max(-phimax, min(u[1], phimax)) + + # Return the derivative of the state + return np.array([ + math.cos(x[2]) * u[0], # xdot = cos(theta) v + math.sin(x[2]) * u[0], # ydot = sin(theta) v + (u[0] / l) * math.tan(phi) # thdot = v/l tan(phi) + ]) + +def vehicle_output(t, x, u, params): + return x # return x, y, theta (full state) + +# Flatness structure +def vehicle_forward(x, u, params={}): + b = params.get('wheelbase', 3.) # get parameter values + zflag = [np.zeros(3), np.zeros(3)] # list for flag arrays + zflag[0][0] = x[0] # flat outputs + zflag[1][0] = x[1] + zflag[0][1] = u[0] * np.cos(x[2]) # first derivatives + zflag[1][1] = u[0] * np.sin(x[2]) + thdot = (u[0]/b) * np.tan(u[1]) # dtheta/dt + zflag[0][2] = -u[0] * thdot * np.sin(x[2]) # second derivatives + zflag[1][2] = u[0] * thdot * np.cos(x[2]) + return zflag + +def vehicle_reverse(zflag, params={}): + b = params.get('wheelbase', 3.) # get parameter values + x = np.zeros(3); u = np.zeros(2) # vectors to store x, u + x[0] = zflag[0][0] # x position + x[1] = zflag[1][0] # y position + x[2] = np.arctan2(zflag[1][1], zflag[0][1]) # angle + u[0] = zflag[0][1] * np.cos(x[2]) + zflag[1][1] * np.sin(x[2]) + thdot_v = zflag[1][2] * np.cos(x[2]) - zflag[0][2] * np.sin(x[2]) + u[1] = np.arctan2(thdot_v, u[0]**2 / b) + return x, u + +vehicle = flat.FlatSystem( + vehicle_forward, vehicle_reverse, vehicle_update, + vehicle_output, inputs=('v', 'delta'), outputs=('x', 'y', 'theta'), + states=('x', 'y', 'theta')) + +# Initial and final conditions +x0 = [0., -2., 0.]; u0 = [10., 0.] +xf = [100., 2., 0.]; uf = [10., 0.] +Tf = 10 + +# Define the time points where the cost/constraints will be evaluated +timepts = np.linspace(0, Tf, 10, endpoint=True) + +def time_steering_point_to_point(basis_name, basis_size): + if basis_name == 'poly': + basis = flat.PolyFamily(basis_size) + elif basis_name == 'bezier': + basis = flat.BezierFamily(basis_size) + + # Find trajectory between initial and final conditions + traj = flat.point_to_point(vehicle, Tf, x0, u0, xf, uf, basis=basis) + + # Verify that the trajectory computation is correct + x, u = traj.eval([0, Tf]) + np.testing.assert_array_almost_equal(x0, x[:, 0]) + np.testing.assert_array_almost_equal(u0, u[:, 0]) + np.testing.assert_array_almost_equal(xf, x[:, 1]) + np.testing.assert_array_almost_equal(uf, u[:, 1]) + +time_steering_point_to_point.params = (['poly', 'bezier'], [6, 8]) +time_steering_point_to_point.param_names = ["basis", "size"] + +def time_steering_cost(): + # Define cost and constraints + traj_cost = opt.quadratic_cost( + vehicle, None, np.diag([0.1, 1]), u0=uf) + constraints = [ + opt.input_range_constraint(vehicle, [8, -0.1], [12, 0.1]) ] + + traj = flat.point_to_point( + vehicle, timepts, x0, u0, xf, uf, + cost=traj_cost, constraints=constraints, basis=flat.PolyFamily(8) + ) + + # Verify that the trajectory computation is correct + x, u = traj.eval([0, Tf]) + np.testing.assert_array_almost_equal(x0, x[:, 0]) + np.testing.assert_array_almost_equal(u0, u[:, 0]) + np.testing.assert_array_almost_equal(xf, x[:, 1]) + np.testing.assert_array_almost_equal(uf, u[:, 1]) + diff --git a/benchmarks/optimal_bench.py b/benchmarks/optimal_bench.py index 4b34ef04d..21cabef7e 100644 --- a/benchmarks/optimal_bench.py +++ b/benchmarks/optimal_bench.py @@ -1,5 +1,5 @@ # optimal_bench.py - benchmarks for optimal control package -# RMM, 27 Feb 2020 +# RMM, 27 Feb 2021 # # This benchmark tests the timing for the optimal control module # (control.optimal) and is intended to be used for helping tune the @@ -10,10 +10,6 @@ import control as ct import control.flatsys as flat import control.optimal as opt -import matplotlib.pyplot as plt -import logging -import time -import os # Vehicle steering dynamics def vehicle_update(t, x, u, params): diff --git a/control/flatsys/bezier.py b/control/flatsys/bezier.py index 1eb7a549f..5d0d551de 100644 --- a/control/flatsys/bezier.py +++ b/control/flatsys/bezier.py @@ -39,7 +39,7 @@ # SUCH DAMAGE. import numpy as np -from scipy.special import binom +from scipy.special import binom, factorial from .basis import BasisFamily class BezierFamily(BasisFamily): @@ -48,7 +48,9 @@ class BezierFamily(BasisFamily): This class represents the family of polynomials of the form .. math:: - \phi_i(t) = \sum_{i=0}^n {n \choose i} (T - t)^{n-i} t^i + \phi_i(t) = \sum_{i=0}^n {n \choose i} + \left( \frac{t}{T_\text{f}} - t \right)^{n-i} + \left( \frac{t}{T_f} \right)^i """ def __init__(self, N, T=1): @@ -59,11 +61,23 @@ def __init__(self, N, T=1): # Compute the kth derivative of the ith basis function at time t def eval_deriv(self, i, k, t): """Evaluate the kth derivative of the ith basis function at time t.""" - if k > 0: - raise NotImplementedError("Bezier derivatives not yet available") - elif i > self.N: + if i >= self.N: raise ValueError("Basis function index too high") + elif k >= self.N: + # Higher order derivatives are zero + return np.zeros(t.shape) - # Return the Bezier basis function (note N = # basis functions) - return binom(self.N - 1, i) * \ - (t/self.T)**i * (1 - t/self.T)**(self.N - i - 1) + # Compute the variables used in Bezier curve formulas + n = self.N - 1 + u = t/self.T + + if k == 0: + # No derivative => avoid expansion for speed + return binom(n, i) * u**i * (1-u)**(n-i) + + # Return the kth derivative of the ith Bezier basis function + return binom(n, i) * sum([ + (-1)**(j-i) * + binom(n-i, j-i) * factorial(j)/factorial(j-k) * np.power(u, j-k) + for j in range(max(i, k), n+1) + ]) diff --git a/control/flatsys/flatsys.py b/control/flatsys/flatsys.py index a5dec2950..1905c4cb8 100644 --- a/control/flatsys/flatsys.py +++ b/control/flatsys/flatsys.py @@ -38,10 +38,15 @@ # OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF # SUCH DAMAGE. +import itertools import numpy as np +import scipy as sp +import scipy.optimize +import warnings from .poly import PolyFamily from .systraj import SystemTrajectory from ..iosys import NonlinearIOSystem +from ..timeresp import _check_convert_array # Flat system class (for use as a base class) @@ -150,6 +155,8 @@ def __init__(self, if forward is not None: self.forward = forward if reverse is not None: self.reverse = reverse + # Save the length of the flat flag + def forward(self, x, u, params={}): """Compute the flat flag given the states and input. @@ -176,7 +183,7 @@ def forward(self, x, u, params={}): output and its first :math:`q_i` derivatives. """ - pass + raise NotImplementedError("internal error; forward method not defined") def reverse(self, zflag, params={}): """Compute the states and input given the flat flag. @@ -200,7 +207,7 @@ def reverse(self, zflag, params={}): The input to the system corresponding to the flat flag. """ - pass + raise NotImplementedError("internal error; reverse method not defined") def _flat_updfcn(self, t, x, u, params={}): # TODO: implement state space update using flat coordinates @@ -212,8 +219,33 @@ def _flat_outfcn(self, t, x, u, params={}): return np.array(zflag[:][0]) -# Solve a point to point trajectory generation problem for a linear system -def point_to_point(sys, x0, u0, xf, uf, Tf, T0=0, basis=None, cost=None): +# Utility function to compute flag matrix given a basis +def _basis_flag_matrix(sys, basis, flag, t, params={}): + """Compute the matrix of basis functions and their derivatives + + This function computes the matrix ``M`` that is used to solve for the + coefficients of the basis functions given the state and input. Each + column of the matrix corresponds to a basis function and each row is a + derivative, with the derivatives (flag) for each output stacked on top + of each other. + + """ + flagshape = [len(f) for f in flag] + M = np.zeros((sum(flagshape), basis.N * sys.ninputs)) + flag_off = 0 + coeff_off = 0 + for i, flag_len in enumerate(flagshape): + for j, k in itertools.product(range(basis.N), range(flag_len)): + M[flag_off + k, coeff_off + j] = basis.eval_deriv(j, k, t) + flag_off += flag_len + coeff_off += basis.N + return M + + +# Solve a point to point trajectory generation problem for a flat system +def point_to_point( + sys, timepts, x0=0, u0=0, xf=0, uf=0, T0=0, basis=None, cost=None, + constraints=None, initial_guess=None, minimize_kwargs={}, **kwargs): """Compute trajectory between an initial and final conditions. Compute a feasible trajectory for a differentially flat system between an @@ -223,57 +255,109 @@ def point_to_point(sys, x0, u0, xf, uf, Tf, T0=0, basis=None, cost=None): ---------- flatsys : FlatSystem object Description of the differentially flat system. This object must - define a function flatsys.forward() that takes the system state and - produceds the flag of flat outputs and a system flatsys.reverse() + define a function `flatsys.forward()` that takes the system state and + produceds the flag of flat outputs and a system `flatsys.reverse()` that takes the flag of the flat output and prodes the state and input. + timepts : float or 1D array_like + The list of points for evaluating cost and constraints, as well as + the time horizon. If given as a float, indicates the final time for + the trajectory (corresponding to xf) + x0, u0, xf, uf : 1D arrays Define the desired initial and final conditions for the system. If any of the values are given as None, they are replaced by a vector of zeros of the appropriate dimension. - Tf : float - The final time for the trajectory (corresponding to xf) - - T0 : float (optional) + T0 : float, optional The initial time for the trajectory (corresponding to x0). If not specified, its value is taken to be zero. - basis : BasisFamily object (optional) + basis : :class:`~control.flatsys.BasisFamily` object, optional The basis functions to use for generating the trajectory. If not - specified, the PolyFamily basis family will be used, with the minimal - number of elements required to find a feasible trajectory (twice - the number of system states) + specified, the :class:`~control.flatsys.PolyFamily` basis family + will be used, with the minimal number of elements required to find a + feasible trajectory (twice the number of system states) + + cost : callable + Function that returns the integral cost given the current state + and input. Called as `cost(x, u)`. + + constraints : list of tuples, optional + List of constraints that should hold at each point in the time vector. + Each element of the list should consist of a tuple with first element + given by :class:`scipy.optimize.LinearConstraint` or + :class:`scipy.optimize.NonlinearConstraint` and the remaining + elements of the tuple are the arguments that would be passed to those + functions. The following tuples are supported: + + * (LinearConstraint, A, lb, ub): The matrix A is multiplied by stacked + vector of the state and input at each point on the trajectory for + comparison against the upper and lower bounds. + + * (NonlinearConstraint, fun, lb, ub): a user-specific constraint + function `fun(x, u)` is called at each point along the trajectory + and compared against the upper and lower bounds. + + The constraints are applied at each time point along the trajectory. + + minimize_kwargs : str, optional + Pass additional keywords to :func:`scipy.optimize.minimize`. Returns ------- - traj : SystemTrajectory object + traj : :class:`~control.flatsys.SystemTrajectory` object The system trajectory is returned as an object that implements the - eval() function, we can be used to compute the value of the state + `eval()` function, we can be used to compute the value of the state and input and a given time t. + Notes + ----- + Additional keyword parameters can be used to fine tune the behavior of + the underlying optimization function. See `minimize_*` keywords in + :func:`OptimalControlProblem` for more information. + """ # # Make sure the problem is one that we can handle # - # TODO: put in tests for flat system input - # TODO: process initial and final conditions to allow x0 or (x0, u0) - if x0 is None: x0 = np.zeros(sys.nstates) - if u0 is None: u0 = np.zeros(sys.ninputs) - if xf is None: xf = np.zeros(sys.nstates) - if uf is None: uf = np.zeros(sys.ninputs) + x0 = _check_convert_array(x0, [(sys.nstates,), (sys.nstates, 1)], + 'Initial state: ', squeeze=True) + u0 = _check_convert_array(u0, [(sys.ninputs,), (sys.ninputs, 1)], + 'Initial input: ', squeeze=True) + xf = _check_convert_array(xf, [(sys.nstates,), (sys.nstates, 1)], + 'Final state: ', squeeze=True) + uf = _check_convert_array(uf, [(sys.ninputs,), (sys.ninputs, 1)], + 'Final input: ', squeeze=True) + + # Process final time + timepts = np.atleast_1d(timepts) + Tf = timepts[-1] + T0 = timepts[0] if len(timepts) > 1 else T0 + + # Process keyword arguments + minimize_kwargs['method'] = kwargs.pop('minimize_method', None) + minimize_kwargs['options'] = kwargs.pop('minimize_options', {}) + if kwargs: + raise TypeError("unrecognized keywords: ", str(kwargs)) # # Determine the basis function set to use and make sure it is big enough # # If no basis set was specified, use a polynomial basis (poor choice...) - if (basis is None): basis = PolyFamily(2*sys.nstates, Tf) + if basis is None: + basis = PolyFamily(2 * (sys.nstates + sys.ninputs)) # Make sure we have enough basis functions to solve the problem - if (basis.N * sys.ninputs < 2 * (sys.nstates + sys.ninputs)): + if basis.N * sys.ninputs < 2 * (sys.nstates + sys.ninputs): raise ValueError("basis set is too small") + elif (cost is not None or constraints is not None) and \ + basis.N * sys.ninputs == 2 * (sys.nstates + sys.ninputs): + warnings.warn("minimal basis specified; optimization not possible") + cost = None + constraints = None # # Map the initial and final conditions to flat output conditions @@ -281,8 +365,7 @@ def point_to_point(sys, x0, u0, xf, uf, Tf, T0=0, basis=None, cost=None): # We need to compute the output "flag": [z(t), z'(t), z''(t), ...] # and then evaluate this at the initial and final condition. # - # TODO: should be able to represent flag variables as 1D arrays - # TODO: need inputs to fully define the flag + zflag_T0 = sys.forward(x0, u0) zflag_Tf = sys.forward(xf, uf) @@ -293,54 +376,139 @@ def point_to_point(sys, x0, u0, xf, uf, Tf, T0=0, basis=None, cost=None): # essentially amounts to evaluating the basis functions and their # derivatives at the initial and final conditions. - # Figure out the size of the problem we are solving - flag_tot = np.sum([len(zflag_T0[i]) for i in range(sys.ninputs)]) - - # Start by creating an empty matrix that we can fill up - # TODO: allow a different number of basis elements for each flat output - M = np.zeros((2 * flag_tot, basis.N * sys.ninputs)) - - # Now fill in the rows for the initial and final states - flag_off = 0 - coeff_off = 0 - for i in range(sys.ninputs): - flag_len = len(zflag_T0[i]) - for j in range(basis.N): - for k in range(flag_len): - M[flag_off + k, coeff_off + j] = basis.eval_deriv(j, k, T0) - M[flag_tot + flag_off + k, coeff_off + j] = \ - basis.eval_deriv(j, k, Tf) - flag_off += flag_len - coeff_off += basis.N - - # Create an empty matrix that we can fill up - Z = np.zeros(2 * flag_tot) + # Compute the flags for the initial and final states + M_T0 = _basis_flag_matrix(sys, basis, zflag_T0, T0) + M_Tf = _basis_flag_matrix(sys, basis, zflag_Tf, Tf) - # Compute the flag vector to use for the right hand side by - # stacking up the flags for each input - # TODO: make this more pythonic - flag_off = 0 - for i in range(sys.ninputs): - flag_len = len(zflag_T0[i]) - for j in range(flag_len): - Z[flag_off + j] = zflag_T0[i][j] - Z[flag_tot + flag_off + j] = zflag_Tf[i][j] - flag_off += flag_len + # Stack the initial and final matrix/flag for the point to point problem + M = np.vstack([M_T0, M_Tf]) + Z = np.hstack([np.hstack(zflag_T0), np.hstack(zflag_Tf)]) # # Solve for the coefficients of the flat outputs # # At this point, we need to solve the equation M alpha = zflag, where M # is the matrix constrains for initial and final conditions and zflag = - # [zflag_T0; zflag_tf]. Since everything is linear, just compute the - # least squares solution for now. + # [zflag_T0; zflag_tf]. # - # TODO: need to allow cost and constraints... + # If there are no constraints, then we just need to solve a linear + # system of equations => use least squares. Otherwise, we have a + # nonlinear optimal control problem with equality constraints => use + # scipy.optimize.minimize(). + # + + # Start by solving the least squares problem alpha, residuals, rank, s = np.linalg.lstsq(M, Z, rcond=None) + if cost is not None or constraints is not None: + # Search over the null space to minimize cost/satisfy constraints + N = sp.linalg.null_space(M) + + # Define a function to evaluate the cost along a trajectory + def traj_cost(null_coeffs): + # Add this to the existing solution + coeffs = alpha + N @ null_coeffs + + # Evaluate the costs at the listed time points + costval = 0 + for t in timepts: + M_t = _basis_flag_matrix(sys, basis, zflag_T0, t) + + # Compute flag at this time point + zflag = (M_t @ coeffs).reshape(sys.ninputs, -1) + + # Find states and inputs at the time points + x, u = sys.reverse(zflag) + + # Evaluate the cost at this time point + costval += cost(x, u) + return costval + + # If no cost given, override with magnitude of the coefficients + if cost is None: + traj_cost = lambda coeffs: coeffs @ coeffs + + # Process the constraints we were given + traj_constraints = constraints + if constraints is None: + traj_constraints = [] + elif isinstance(constraints, tuple): + # TODO: Check to make sure this is really a constraint + traj_constraints = [constraints] + elif not isinstance(constraints, list): + raise TypeError("trajectory constraints must be a list") + + # Process constraints + minimize_constraints = [] + if len(traj_constraints) > 0: + # Set up a nonlinear function to evaluate the constraints + def traj_const(null_coeffs): + # Add this to the existing solution + coeffs = alpha + N @ null_coeffs + + # Evaluate the constraints at the listed time points + values = [] + for i, t in enumerate(timepts): + # Calculate the states and inputs for the flat output + M_t = _basis_flag_matrix(sys, basis, zflag_T0, t) + + # Compute flag at this time point + zflag = (M_t @ coeffs).reshape(sys.ninputs, -1) + + # Find states and inputs at the time points + states, inputs = sys.reverse(zflag) + + # Evaluate the constraint function along the trajectory + for type, fun, lb, ub in traj_constraints: + if type == sp.optimize.LinearConstraint: + # `fun` is A matrix associated with polytope... + values.append( + np.dot(fun, np.hstack([states, inputs]))) + elif type == sp.optimize.NonlinearConstraint: + values.append(fun(states, inputs)) + else: + raise TypeError( + "unknown constraint type %s" % type) + return np.array(values).flatten() + + # Store upper and lower bounds + const_lb, const_ub = [], [] + for t in timepts: + for type, fun, lb, ub in traj_constraints: + const_lb.append(lb) + const_ub.append(ub) + const_lb = np.array(const_lb).flatten() + const_ub = np.array(const_ub).flatten() + + # Store the constraint as a nonlinear constraint + minimize_constraints = [sp.optimize.NonlinearConstraint( + traj_const, const_lb, const_ub)] + + # Add initial and terminal constraints + # minimize_constraints += [sp.optimize.LinearConstraint(M, Z, Z)] + + # Process the initial condition + if initial_guess is None: + initial_guess = np.zeros(M.shape[1] - M.shape[0]) + else: + raise NotImplementedError("Initial guess not yet implemented.") + + # Find the optimal solution + res = sp.optimize.minimize( + traj_cost, initial_guess, constraints=minimize_constraints, + **minimize_kwargs) + if res.success: + alpha += N @ res.x + else: + raise RuntimeError( + "Unable to solve optimal control problem\n" + + "scipy.optimize.minimize returned " + res.message) + # # Transform the trajectory from flat outputs to states and inputs # + + # Createa trajectory object to store the resul systraj = SystemTrajectory(sys, basis) # Store the flag lengths and coefficients diff --git a/control/flatsys/linflat.py b/control/flatsys/linflat.py index 41a68537a..6e74ed581 100644 --- a/control/flatsys/linflat.py +++ b/control/flatsys/linflat.py @@ -97,13 +97,14 @@ def __init__(self, linsys, inputs=None, outputs=None, states=None, name=name) # Find the transformation to chain of integrators form + # Note: store all array as ndarray, not matrix zsys, Tr = control.reachable_form(linsys) - Tr = Tr[::-1, ::] # flip rows + Tr = np.array(Tr[::-1, ::]) # flip rows # Extract the information that we need - self.F = zsys.A[0, ::-1] # input function coeffs - self.T = Tr # state space transformation - self.Tinv = np.linalg.inv(Tr) # compute inverse once + self.F = np.array(zsys.A[0, ::-1]) # input function coeffs + self.T = Tr # state space transformation + self.Tinv = np.linalg.inv(Tr) # compute inverse once # Compute the flat output variable z = C x Cfz = np.zeros(np.shape(linsys.C)); Cfz[0, 0] = 1 diff --git a/control/iosys.py b/control/iosys.py index 7ed4c8b05..6dfec1ca1 100644 --- a/control/iosys.py +++ b/control/iosys.py @@ -1423,13 +1423,13 @@ def input_output_response( Parameters ---------- - sys: InputOutputSystem + sys : InputOutputSystem Input/output system to simulate. - T: array-like + T : array-like Time steps at which the input is defined; values must be evenly spaced. - U: array-like or number, optional + U : array-like or number, optional Input array giving input at each time `T` (default = 0). - X0: array-like or number, optional + X0 : array-like or number, optional Initial condition (default = 0). return_x : bool, optional If True, return the values of the state at each time (default = False). @@ -1451,21 +1451,21 @@ def input_output_response( xout : array Time evolution of the state vector (if return_x=True). - Raises - ------ - TypeError - If the system is not an input/output system. - ValueError - If time step does not match sampling time (for discrete time systems) - - Additional parameters - --------------------- + Other parameters + ---------------- solve_ivp_method : str, optional Set the method used by :func:`scipy.integrate.solve_ivp`. Defaults to 'RK45'. solve_ivp_kwargs : str, optional Pass additional keywords to :func:`scipy.integrate.solve_ivp`. + Raises + ------ + TypeError + If the system is not an input/output system. + ValueError + If time step does not match sampling time (for discrete time systems). + """ # # Process keyword arguments diff --git a/control/optimal.py b/control/optimal.py index 9ec25b4fc..63509ef4f 100644 --- a/control/optimal.py +++ b/control/optimal.py @@ -59,7 +59,7 @@ class OptimalControlProblem(): """ def __init__( - self, sys, time_vector, integral_cost, trajectory_constraints=[], + self, sys, timepts, integral_cost, trajectory_constraints=[], terminal_cost=None, terminal_constraints=[], initial_guess=None, basis=None, log=False, **kwargs): """Set up an optimal control problem @@ -73,7 +73,7 @@ def __init__( ---------- sys : InputOutputSystem I/O system for which the optimal input will be computed. - time_vector : 1D array_like + timepts : 1D array_like List of times at which the optimal input should be computed. integral_cost : callable Function that returns the integral cost given the current state @@ -84,8 +84,8 @@ def __init__( first element given by :meth:`~scipy.optimize.LinearConstraint` or :meth:`~scipy.optimize.NonlinearConstraint` and the remaining elements of the tuple are the arguments that would be passed to - those functions. The constrains will be applied at each point - along the trajectory. + those functions. The constraints will be applied at each time + point along the trajectory. terminal_cost : callable, optional Function that returns the terminal cost given the current state and input. Called as terminal_cost(x, u). @@ -121,7 +121,7 @@ def __init__( """ # Save the basic information for use later self.system = sys - self.time_vector = time_vector + self.timepts = timepts self.integral_cost = integral_cost self.terminal_cost = terminal_cost self.terminal_constraints = terminal_constraints @@ -169,9 +169,8 @@ def __init__( constraint_lb, constraint_ub, eqconst_value = [], [], [] # Go through each time point and stack the bounds - for t in self.time_vector: - for constraint in self.trajectory_constraints: - type, fun, lb, ub = constraint + for t in self.timepts: + for type, fun, lb, ub in self.trajectory_constraints: if np.all(lb == ub): # Equality constraint eqconst_value.append(lb) @@ -181,8 +180,7 @@ def __init__( constraint_ub.append(ub) # Add on the terminal constraints - for constraint in self.terminal_constraints: - type, fun, lb, ub = constraint + for type, fun, lb, ub in self.terminal_constraints: if np.all(lb == ub): # Equality constraint eqconst_value.append(lb) @@ -263,7 +261,7 @@ def _cost_function(self, coeffs): # Simulate the system to get the state _, _, states = ct.input_output_response( - self.system, self.time_vector, inputs, x, return_x=True, + self.system, self.timepts, inputs, x, return_x=True, solve_ivp_kwargs=self.solve_ivp_kwargs) self.system_simulations += 1 self.last_x = x @@ -279,14 +277,14 @@ def _cost_function(self, coeffs): if ct.isctime(self.system): # Evaluate the costs costs = [self.integral_cost(states[:, i], inputs[:, i]) for - i in range(self.time_vector.size)] + i in range(self.timepts.size)] # Compute the time intervals - dt = np.diff(self.time_vector) + dt = np.diff(self.timepts) # Integrate the cost cost = 0 - for i in range(self.time_vector.size-1): + for i in range(self.timepts.size-1): # Approximate the integral using trapezoidal rule cost += 0.5 * (costs[i] + costs[i+1]) * dt[i] @@ -320,19 +318,19 @@ def _cost_function(self, coeffs): # constraints, which each take inputs [x, u] and evaluate the # constraint. How we handle these depends on the type of constraint: # - # * For linear constraints (LinearConstraint), a combined vector of the - # state and input is multiplied by the polytope A matrix for - # comparison against the upper and lower bounds. + # * For linear constraints (LinearConstraint), a combined (hstack'd) + # vector of the state and input is multiplied by the polytope A matrix + # for comparison against the upper and lower bounds. # # * For nonlinear constraints (NonlinearConstraint), a user-specific # constraint function having the form # - # constraint_fun(x, u) TODO: convert from [x, u] to (x, u) + # constraint_fun(x, u) # # is called at each point along the trajectory and compared against the # upper and lower bounds. # - # * If the upper and lower bound for the constraint is identical, then we + # * If the upper and lower bound for the constraint are identical, then we # separate out the evaluation into two different constraints, which # allows the SciPy optimizers to be more efficient (and stops them from # generating a warning about mixed constraints). This is handled @@ -383,7 +381,7 @@ def _constraint_function(self, coeffs): # Simulate the system to get the state _, _, states = ct.input_output_response( - self.system, self.time_vector, inputs, x, return_x=True, + self.system, self.timepts, inputs, x, return_x=True, solve_ivp_kwargs=self.solve_ivp_kwargs) self.system_simulations += 1 self.last_x = x @@ -392,9 +390,8 @@ def _constraint_function(self, coeffs): # Evaluate the constraint function along the trajectory value = [] - for i, t in enumerate(self.time_vector): - for constraint in self.trajectory_constraints: - type, fun, lb, ub = constraint + for i, t in enumerate(self.timepts): + for type, fun, lb, ub in self.trajectory_constraints: if np.all(lb == ub): # Skip equality constraints continue @@ -409,8 +406,7 @@ def _constraint_function(self, coeffs): constraint[0]) # Evaluate the terminal constraint functions - for constraint in self.terminal_constraints: - type, fun, lb, ub = constraint + for type, fun, lb, ub in self.terminal_constraints: if np.all(lb == ub): # Skip equality constraints continue @@ -469,7 +465,7 @@ def _eqconst_function(self, coeffs): # Simulate the system to get the state _, _, states = ct.input_output_response( - self.system, self.time_vector, inputs, x, return_x=True, + self.system, self.timepts, inputs, x, return_x=True, solve_ivp_kwargs=self.solve_ivp_kwargs) self.system_simulations += 1 self.last_x = x @@ -482,9 +478,8 @@ def _eqconst_function(self, coeffs): # Evaluate the constraint function along the trajectory value = [] - for i, t in enumerate(self.time_vector): - for constraint in self.trajectory_constraints: - type, fun, lb, ub = constraint + for i, t in enumerate(self.timepts): + for type, fun, lb, ub in self.trajectory_constraints: if np.any(lb != ub): # Skip inequality constraints continue @@ -499,8 +494,7 @@ def _eqconst_function(self, coeffs): constraint[0]) # Evaluate the terminal constraint functions - for constraint in self.terminal_constraints: - type, fun, lb, ub = constraint + for type, fun, lb, ub in self.terminal_constraints: if np.any(lb != ub): # Skip inequality constraints continue @@ -552,12 +546,12 @@ def _process_initial_guess(self, initial_guess): try: initial_guess = np.broadcast_to( initial_guess.reshape(-1, 1), - (self.system.ninputs, self.time_vector.size)) + (self.system.ninputs, self.timepts.size)) except ValueError: raise ValueError("initial guess is the wrong shape") elif initial_guess.shape != \ - (self.system.ninputs, self.time_vector.size): + (self.system.ninputs, self.timepts.size): raise ValueError("initial guess is the wrong shape") # If we were given a basis, project onto the basis elements @@ -570,7 +564,7 @@ def _process_initial_guess(self, initial_guess): # Default is zero return np.zeros( self.system.ninputs * - (self.time_vector.size if self.basis is None else self.basis.N)) + (self.timepts.size if self.basis is None else self.basis.N)) # # Utility function to convert input vector to coefficient vector @@ -590,12 +584,12 @@ def _inputs_to_coeffs(self, inputs): coeffs = np.zeros((self.system.ninputs, self.basis.N)) for i in range(self.system.ninputs): # Set up the matrices to get inputs - M = np.zeros((self.time_vector.size, self.basis.N)) - b = np.zeros(self.time_vector.size) + M = np.zeros((self.timepts.size, self.basis.N)) + b = np.zeros(self.timepts.size) # Evaluate at each time point and for each basis function # TODO: vectorize - for j, t in enumerate(self.time_vector): + for j, t in enumerate(self.timepts): for k in range(self.basis.N): M[j, k] = self.basis(k, t) b[j] = inputs[i, j] @@ -609,8 +603,8 @@ def _inputs_to_coeffs(self, inputs): # Utility function to convert coefficient vector to input vector def _coeffs_to_inputs(self, coeffs): # TODO: vectorize - inputs = np.zeros((self.system.ninputs, self.time_vector.size)) - for i, t in enumerate(self.time_vector): + inputs = np.zeros((self.system.ninputs, self.timepts.size)) + for i, t in enumerate(self.timepts): for k in range(self.basis.N): phi_k = self.basis(k, t) for inp in range(self.system.ninputs): @@ -680,7 +674,7 @@ def _output(t, x, u, params={}): _update, _output, dt=dt, inputs=self.system.nstates, outputs=self.system.ninputs, states=self.system.ninputs * - (self.time_vector.size if self.basis is None else self.basis.N)) + (self.timepts.size if self.basis is None else self.basis.N)) # Compute the optimal trajectory from the current state def compute_trajectory( @@ -827,17 +821,17 @@ def __init__( if print_summary: ocp._print_statistics() - if return_states and inputs.shape[1] == ocp.time_vector.shape[0]: + if return_states and inputs.shape[1] == ocp.timepts.shape[0]: # Simulate the system if we need the state back _, _, states = ct.input_output_response( - ocp.system, ocp.time_vector, inputs, ocp.x, return_x=True, + ocp.system, ocp.timepts, inputs, ocp.x, return_x=True, solve_ivp_kwargs=ocp.solve_ivp_kwargs) ocp.system_simulations += 1 else: states = None retval = _process_time_response( - ocp.system, ocp.time_vector, inputs, states, + ocp.system, ocp.timepts, inputs, states, transpose=transpose, return_x=return_states, squeeze=squeeze) self.time = retval[0] @@ -866,7 +860,7 @@ def solve_ocp( cost : callable Function that returns the integral cost given the current state - and input. Called as cost(x, u). + and input. Called as `cost(x, u)`. constraints : list of tuples, optional List of constraints that should hold at each point in the time vector. @@ -884,7 +878,7 @@ def solve_ocp( function `fun(x, u)` is called at each point along the trajectory and compared against the upper and lower bounds. - The constraints are applied at each point along the trajectory. + The constraints are applied at each time point along the trajectory. terminal_cost : callable, optional Function that returns the terminal cost given the current state diff --git a/control/tests/flatsys_test.py b/control/tests/flatsys_test.py index 0239d9455..373af8dae 100644 --- a/control/tests/flatsys_test.py +++ b/control/tests/flatsys_test.py @@ -16,7 +16,7 @@ import control as ct import control.flatsys as fs - +import control.optimal as opt class TestFlatSys: """Test differential flat systems""" @@ -35,7 +35,7 @@ def test_double_integrator(self, xf, uf, Tf): poly = fs.PolyFamily(6) x1, u1, = [0, 0], [0] - traj = fs.point_to_point(flatsys, x1, u1, xf, uf, Tf, basis=poly) + traj = fs.point_to_point(flatsys, Tf, x1, u1, xf, uf, basis=poly) # Verify that the trajectory computation is correct x, u = traj.eval([0, Tf]) @@ -51,7 +51,8 @@ def test_double_integrator(self, xf, uf, Tf): t, y, x = ct.forced_response(sys, T, ud, x1, return_x=True) np.testing.assert_array_almost_equal(x, xd, decimal=3) - def test_kinematic_car(self): + @pytest.fixture + def vehicle_flat(self): """Differential flatness for a kinematic car""" def vehicle_flat_forward(x, u, params={}): b = params.get('wheelbase', 3.) # get parameter values @@ -88,21 +89,21 @@ def vehicle_update(t, x, u, params): def vehicle_output(t, x, u, params): return x # Create differentially flat input/output system - vehicle_flat = fs.FlatSystem( + return fs.FlatSystem( vehicle_flat_forward, vehicle_flat_reverse, vehicle_update, vehicle_output, inputs=('v', 'delta'), outputs=('x', 'y', 'theta'), states=('x', 'y', 'theta')) + @pytest.mark.parametrize("poly", [ + fs.PolyFamily(6), fs.PolyFamily(8), fs.BezierFamily(6)]) + def test_kinematic_car(self, vehicle_flat, poly): # Define the endpoints of the trajectory x0 = [0., -2., 0.]; u0 = [10., 0.] xf = [100., 2., 0.]; uf = [10., 0.] Tf = 10 - # Define a set of basis functions to use for the trajectories - poly = fs.PolyFamily(6) - # Find trajectory between initial and final conditions - traj = fs.point_to_point(vehicle_flat, x0, u0, xf, uf, Tf, basis=poly) + traj = fs.point_to_point(vehicle_flat, Tf, x0, u0, xf, uf, basis=poly) # Verify that the trajectory computation is correct x, u = traj.eval([0, Tf]) @@ -121,3 +122,227 @@ def vehicle_output(t, x, u, params): return x vehicle_flat, T, ud, x0, return_x=True) np.testing.assert_allclose(x, xd, atol=0.01, rtol=0.01) + def test_flat_cost_constr(self): + # Double integrator system + sys = ct.ss([[0, 1], [0, 0]], [[0], [1]], [[1, 0]], 0) + flat_sys = fs.LinearFlatSystem(sys) + + # Define the endpoints of the trajectory + x0 = [1, 0]; u0 = [0] + xf = [0, 0]; uf = [0] + Tf = 10 + T = np.linspace(0, Tf, 500) + + # Find trajectory between initial and final conditions + traj = fs.point_to_point( + flat_sys, Tf, x0, u0, xf, uf, basis=fs.PolyFamily(8)) + x, u = traj.eval(T) + + np.testing.assert_array_almost_equal(x0, x[:, 0]) + np.testing.assert_array_almost_equal(u0, u[:, 0]) + np.testing.assert_array_almost_equal(xf, x[:, -1]) + np.testing.assert_array_almost_equal(uf, u[:, -1]) + + # Solve with a cost function + timepts = np.linspace(0, Tf, 10) + cost_fcn = opt.quadratic_cost( + flat_sys, np.diag([0, 0]), 1, x0=xf, u0=uf) + + traj_cost = fs.point_to_point( + flat_sys, timepts, x0, u0, xf, uf, cost=cost_fcn, + basis=fs.PolyFamily(8), + # initial_guess='lstsq', + # minimize_kwargs={'method': 'trust-constr'} + ) + + # Verify that the trajectory computation is correct + x_cost, u_cost = traj_cost.eval(T) + np.testing.assert_array_almost_equal(x0, x_cost[:, 0]) + np.testing.assert_array_almost_equal(u0, u_cost[:, 0]) + np.testing.assert_array_almost_equal(xf, x_cost[:, -1]) + np.testing.assert_array_almost_equal(uf, u_cost[:, -1]) + + # Make sure that we got a different answer than before + assert np.any(np.abs(x - x_cost) > 0.1) + + # Re-solve with constraint on the y deviation + lb, ub = [-2, -0.1], [2, 0] + lb, ub = [-2, np.min(x_cost[1])*0.95], [2, 1] + constraints = [opt.state_range_constraint(flat_sys, lb, ub)] + + # Make sure that the previous solution violated at least one constraint + assert np.any(x_cost[0, :] < lb[0]) or np.any(x_cost[0, :] > ub[0]) \ + or np.any(x_cost[1, :] < lb[1]) or np.any(x_cost[1, :] > ub[1]) + + traj_const = fs.point_to_point( + flat_sys, timepts, x0, u0, xf, uf, cost=cost_fcn, + constraints=constraints, basis=fs.PolyFamily(8), + ) + + # Verify that the trajectory computation is correct + x_const, u_const = traj_const.eval(T) + np.testing.assert_array_almost_equal(x0, x_const[:, 0]) + np.testing.assert_array_almost_equal(u0, u_const[:, 0]) + np.testing.assert_array_almost_equal(xf, x_const[:, -1]) + np.testing.assert_array_almost_equal(uf, u_const[:, -1]) + + # Make sure that the solution respects the bounds (with some slop) + for i in range(x_const.shape[0]): + assert np.all(x_const[i] >= lb[i] * 1.02) + assert np.all(x_const[i] <= ub[i] * 1.02) + + # Solve the same problem with a nonlinear constraint type + nl_constraints = [ + (sp.optimize.NonlinearConstraint, lambda x, u: x, lb, ub)] + traj_nlconst = fs.point_to_point( + flat_sys, timepts, x0, u0, xf, uf, cost=cost_fcn, + constraints=nl_constraints, basis=fs.PolyFamily(8), + ) + x_nlconst, u_nlconst = traj_nlconst.eval(T) + np.testing.assert_almost_equal(x_const, x_nlconst) + np.testing.assert_almost_equal(u_const, u_nlconst) + + def test_bezier_basis(self): + bezier = fs.BezierFamily(4) + time = np.linspace(0, 1, 100) + + # Sum of the Bezier curves should be one + np.testing.assert_almost_equal( + 1, sum([bezier(i, time) for i in range(4)])) + + # Sum of derivatives should be zero + for k in range(1, 5): + np.testing.assert_almost_equal( + 0, sum([bezier.eval_deriv(i, k, time) for i in range(4)])) + + # Compare derivatives to formulas + np.testing.assert_almost_equal( + bezier.eval_deriv(1, 0, time), 3 * time - 6 * time**2 + 3 * time**3) + np.testing.assert_almost_equal( + bezier.eval_deriv(1, 1, time), 3 - 12 * time + 9 * time**2) + np.testing.assert_almost_equal( + bezier.eval_deriv(1, 2, time), -12 + 18 * time) + + # Make sure that the second derivative integrates to the first + time = np.linspace(0, 1, 1000) + dt = np.diff(time) + for N in range(5): + bezier = fs.BezierFamily(N) + for i in range(N): + for j in range(1, N+1): + np.testing.assert_allclose( + np.diff(bezier.eval_deriv(i, j-1, time)) / dt, + bezier.eval_deriv(i, j, time)[0:-1], + atol=0.01, rtol=0.01) + + # Exception check + with pytest.raises(ValueError, match="index too high"): + bezier.eval_deriv(4, 0, time) + + def test_point_to_point_errors(self): + """Test error and warning conditions in point_to_point()""" + # Double integrator system + sys = ct.ss([[0, 1], [0, 0]], [[0], [1]], [[1, 0]], 0) + flat_sys = fs.LinearFlatSystem(sys) + + # Define the endpoints of the trajectory + x0 = [1, 0]; u0 = [0] + xf = [0, 0]; uf = [0] + Tf = 10 + T = np.linspace(0, Tf, 500) + + # Cost function + timepts = np.linspace(0, Tf, 10) + cost_fcn = opt.quadratic_cost( + flat_sys, np.diag([1, 1]), 1, x0=xf, u0=uf) + + # Solving without basis specified should be OK + traj = fs.point_to_point(flat_sys, timepts, x0, u0, xf, uf) + x, u = traj.eval(timepts) + np.testing.assert_array_almost_equal(x0, x[:, 0]) + np.testing.assert_array_almost_equal(u0, u[:, 0]) + np.testing.assert_array_almost_equal(xf, x[:, -1]) + np.testing.assert_array_almost_equal(uf, u[:, -1]) + + # Adding a cost function generates a warning + with pytest.warns(UserWarning, match="optimization not possible"): + traj = fs.point_to_point( + flat_sys, timepts, x0, u0, xf, uf, cost=cost_fcn) + + # Make sure we still solved the problem + x, u = traj.eval(timepts) + np.testing.assert_array_almost_equal(x0, x[:, 0]) + np.testing.assert_array_almost_equal(u0, u[:, 0]) + np.testing.assert_array_almost_equal(xf, x[:, -1]) + np.testing.assert_array_almost_equal(uf, u[:, -1]) + + # Try to optimize with insufficient degrees of freedom + with pytest.warns(UserWarning, match="optimization not possible"): + traj = fs.point_to_point( + flat_sys, timepts, x0, u0, xf, uf, cost=cost_fcn, + basis=fs.PolyFamily(6)) + + # Make sure we still solved the problem + x, u = traj.eval(timepts) + np.testing.assert_array_almost_equal(x0, x[:, 0]) + np.testing.assert_array_almost_equal(u0, u[:, 0]) + np.testing.assert_array_almost_equal(xf, x[:, -1]) + np.testing.assert_array_almost_equal(uf, u[:, -1]) + + # Solve with the errors in the various input arguments + with pytest.raises(ValueError, match="Initial state: Wrong shape"): + traj = fs.point_to_point(flat_sys, timepts, np.zeros(3), u0, xf, uf) + with pytest.raises(ValueError, match="Initial input: Wrong shape"): + traj = fs.point_to_point(flat_sys, timepts, x0, np.zeros(3), xf, uf) + with pytest.raises(ValueError, match="Final state: Wrong shape"): + traj = fs.point_to_point(flat_sys, timepts, x0, u0, np.zeros(3), uf) + with pytest.raises(ValueError, match="Final input: Wrong shape"): + traj = fs.point_to_point(flat_sys, timepts, x0, u0, xf, np.zeros(3)) + + # Different ways of describing constraints + constraint = opt.input_range_constraint(flat_sys, -100, 100) + + with pytest.warns(UserWarning, match="optimization not possible"): + traj = fs.point_to_point( + flat_sys, timepts, x0, u0, xf, uf, constraints=constraint, + basis=fs.PolyFamily(6)) + + x, u = traj.eval(timepts) + np.testing.assert_array_almost_equal(x0, x[:, 0]) + np.testing.assert_array_almost_equal(u0, u[:, 0]) + np.testing.assert_array_almost_equal(xf, x[:, -1]) + np.testing.assert_array_almost_equal(uf, u[:, -1]) + + # Constraint that isn't a constraint + with pytest.raises(TypeError, match="must be a list"): + traj = fs.point_to_point( + flat_sys, timepts, x0, u0, xf, uf, constraints=np.eye(2), + basis=fs.PolyFamily(8)) + + # Unknown constraint type + with pytest.raises(TypeError, match="unknown constraint type"): + traj = fs.point_to_point( + flat_sys, timepts, x0, u0, xf, uf, + constraints=[(None, 0, 0, 0)], basis=fs.PolyFamily(8)) + + # Unsolvable optimization + constraint = [opt.input_range_constraint(flat_sys, -0.01, 0.01)] + with pytest.raises(RuntimeError, match="Unable to solve optimal"): + traj = fs.point_to_point( + flat_sys, timepts, x0, u0, xf, uf, constraints=constraint, + basis=fs.PolyFamily(8)) + + # Method arguments, parameters + traj_method = fs.point_to_point( + flat_sys, timepts, x0, u0, xf, uf, cost=cost_fcn, + basis=fs.PolyFamily(8), minimize_method='slsqp') + traj_kwarg = fs.point_to_point( + flat_sys, timepts, x0, u0, xf, uf, cost=cost_fcn, + basis=fs.PolyFamily(8), minimize_kwargs={'method': 'slsqp'}) + np.testing.assert_almost_equal( + traj_method.eval(timepts)[0], traj_kwarg.eval(timepts)[0]) + + # Unrecognized keywords + with pytest.raises(TypeError, match="unrecognized keyword"): + traj_method = fs.point_to_point( + flat_sys, timepts, x0, u0, xf, uf, solve_ivp_method=None) diff --git a/control/tests/optimal_test.py b/control/tests/optimal_test.py index d4b3fd6ef..528313e9d 100644 --- a/control/tests/optimal_test.py +++ b/control/tests/optimal_test.py @@ -459,7 +459,7 @@ def test_optimal_basis_simple(): sys, time, x0, cost, constraints, initial_guess=0.99*res1.inputs, basis=flat.BezierFamily(4, Tf), return_x=True) assert res2.success - np.testing.assert_almost_equal(res2.inputs, res1.inputs, decimal=3) + np.testing.assert_allclose(res2.inputs, res1.inputs, atol=0.01, rtol=0.01) # Run with logging turned on for code coverage res3 = opt.solve_ocp( diff --git a/doc/flatsys.rst b/doc/flatsys.rst index f085347a6..b6d2fe962 100644 --- a/doc/flatsys.rst +++ b/doc/flatsys.rst @@ -132,37 +132,42 @@ and their derivatives up to order :math:`q_i`: The number of flat outputs must match the number of system inputs. For a linear system, a flat system representation can be generated using the -:class:`~control.flatsys.LinearFlatSystem` class: +:class:`~control.flatsys.LinearFlatSystem` class:: - flatsys = control.flatsys.LinearFlatSystem(linsys) + sys = control.flatsys.LinearFlatSystem(linsys) -For more general systems, the `FlatSystem` object must be created manually +For more general systems, the `FlatSystem` object must be created manually:: - flatsys = control.flatsys.FlatSystem(nstate, ninputs, forward, reverse) + sys = control.flatsys.FlatSystem(nstate, ninputs, forward, reverse) -In addition to the flat system descriptionn, a set of basis functions +In addition to the flat system description, a set of basis functions :math:`\phi_i(t)` must be chosen. The `FlatBasis` class is used to represent the basis functions. A polynomial basis function of the form 1, :math:`t`, :math:`t^2`, ... can be computed using the `PolyBasis` class, which is -initialized by passing the desired order of the polynomial basis set: +initialized by passing the desired order of the polynomial basis set:: polybasis = control.flatsys.PolyBasis(N) Once the system and basis function have been defined, the :func:`~control.flatsys.point_to_point` function can be used to compute a -trajectory between initial and final states and inputs: +trajectory between initial and final states and inputs:: - traj = control.flatsys.point_to_point(x0, u0, xf, uf, Tf, basis=polybasis) + traj = control.flatsys.point_to_point( + sys, Tf, x0, u0, xf, uf, basis=polybasis) The returned object has class :class:`~control.flatsys.SystemTrajectory` and can be used to compute the state and input trajectory between the initial and -final condition: +final condition:: xd, ud = traj.eval(T) where `T` is a list of times on which the trajectory should be evaluated (e.g., `T = numpy.linspace(0, Tf, M)`. +The :func:`~control.flatsys.point_to_point` function also allows the +specification of a cost function and/or constraints, in the same +format as :func:`~control.optimal.solve_ocp`. + Example ======= @@ -241,7 +246,7 @@ the endpoints. poly = fs.PolyFamily(6) # Find a trajectory between the initial condition and the final condition - traj = fs.point_to_point(vehicle_flat, x0, u0, xf, uf, Tf, basis=poly) + traj = fs.point_to_point(vehicle_flat, Tf, x0, u0, xf, uf, basis=poly) # Create the trajectory t = np.linspace(0, Tf, 100) @@ -256,6 +261,7 @@ Flat systems classes :toctree: generated/ BasisFamily + BezierFamily FlatSystem LinearFlatSystem PolyFamily diff --git a/examples/kincar-flatsys.py b/examples/kincar-flatsys.py index 17a1b71b9..ca2a946ed 100644 --- a/examples/kincar-flatsys.py +++ b/examples/kincar-flatsys.py @@ -10,7 +10,11 @@ import matplotlib.pyplot as plt import control as ct import control.flatsys as fs +import control.optimal as opt +# +# System model and utility functions +# # Function to take states, inputs and return the flat flag def vehicle_flat_forward(x, u, params={}): @@ -59,7 +63,6 @@ def vehicle_flat_reverse(zflag, params={}): return x, u - # Function to compute the RHS of the system dynamics def vehicle_update(t, x, u, params): b = params.get('wheelbase', 3.) # get parameter values @@ -70,6 +73,38 @@ def vehicle_update(t, x, u, params): ]) return dx +# Plot the trajectory in xy coordinates +def plot_results(t, x, ud): + plt.subplot(4, 1, 2) + plt.plot(x[0], x[1]) + plt.xlabel('x [m]') + plt.ylabel('y [m]') + plt.axis([x0[0], xf[0], x0[1]-1, xf[1]+1]) + + # Time traces of the state and input + plt.subplot(2, 4, 5) + plt.plot(t, x[1]) + plt.ylabel('y [m]') + + plt.subplot(2, 4, 6) + plt.plot(t, x[2]) + plt.ylabel('theta [rad]') + + plt.subplot(2, 4, 7) + plt.plot(t, ud[0]) + plt.xlabel('Time t [sec]') + plt.ylabel('v [m/s]') + plt.axis([0, Tf, u0[0] - 1, uf[0] + 1]) + + plt.subplot(2, 4, 8) + plt.plot(t, ud[1]) + plt.xlabel('Ttime t [sec]') + plt.ylabel('$\delta$ [rad]') + plt.tight_layout() + +# +# Approach 1: point to point solution, no cost or constraints +# # Create differentially flat input/output system vehicle_flat = fs.FlatSystem( @@ -86,7 +121,7 @@ def vehicle_update(t, x, u, params): poly = fs.PolyFamily(6) # Find a trajectory between the initial condition and the final condition -traj = fs.point_to_point(vehicle_flat, x0, u0, xf, uf, Tf, basis=poly) +traj = fs.point_to_point(vehicle_flat, Tf, x0, u0, xf, uf, basis=poly) # Create the desired trajectory between the initial and final condition T = np.linspace(0, Tf, 500) @@ -97,36 +132,51 @@ def vehicle_update(t, x, u, params): vehicle_flat, T, ud, x0, return_x=True) # Plot the open loop system dynamics -plt.figure() +plt.figure(1) plt.suptitle("Open loop trajectory for kinematic car lane change") +plot_results(t, x, ud) -# Plot the trajectory in xy coordinates -plt.subplot(4, 1, 2) -plt.plot(x[0], x[1]) -plt.xlabel('x [m]') -plt.ylabel('y [m]') -plt.axis([x0[0], xf[0], x0[1]-1, xf[1]+1]) - -# Time traces of the state and input -plt.subplot(2, 4, 5) -plt.plot(t, x[1]) -plt.ylabel('y [m]') - -plt.subplot(2, 4, 6) -plt.plot(t, x[2]) -plt.ylabel('theta [rad]') - -plt.subplot(2, 4, 7) -plt.plot(t, ud[0]) -plt.xlabel('Time t [sec]') -plt.ylabel('v [m/s]') -plt.axis([0, Tf, u0[0] - 1, uf[0] + 1]) - -plt.subplot(2, 4, 8) -plt.plot(t, ud[1]) -plt.xlabel('Ttime t [sec]') -plt.ylabel('$\delta$ [rad]') -plt.tight_layout() +# +# Approach #2: add cost function to make lane change quicker +# + +# Define timepoints for evaluation plus basis function to use +timepts = np.linspace(0, Tf, 10) +basis = fs.PolyFamily(8) + +# Define the cost function (penalize lateral error and steering) +traj_cost = opt.quadratic_cost( + vehicle_flat, np.diag([0, 0.1, 0]), np.diag([0.1, 1]), x0=xf, u0=uf) + +# Solve for an optimal solution +traj = fs.point_to_point( + vehicle_flat, timepts, x0, u0, xf, uf, cost=traj_cost, basis=basis, +) +xd, ud = traj.eval(T) + +plt.figure(2) +plt.suptitle("Lane change with lateral error + steering penalties") +plot_results(T, xd, ud) + +# +# Approach #3: optimal cost with trajectory constraints +# +# Resolve the problem with constraints on the inputs +# + +constraints = [ + opt.input_range_constraint(vehicle_flat, [8, -0.1], [12, 0.1]) ] + +# Solve for an optimal solution +traj = fs.point_to_point( + vehicle_flat, timepts, x0, u0, xf, uf, cost=traj_cost, + constraints=constraints, basis=basis, +) +xd, ud = traj.eval(T) + +plt.figure(3) +plt.suptitle("Lane change with penalty + steering constraints") +plot_results(T, xd, ud) # Show the results unless we are running in batch mode if 'PYCONTROL_TEST_EXAMPLES' not in os.environ: diff --git a/examples/steering.ipynb b/examples/steering.ipynb index eb22a5909..217e3b2db 100644 --- a/examples/steering.ipynb +++ b/examples/steering.ipynb @@ -20,8 +20,8 @@ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import control as ct\n", - "ct.use_fbs_defaults()\n", - "ct.use_numpy_matrix(False)" + "import control.optimal as opt\n", + "ct.use_fbs_defaults()" ] }, { @@ -87,40 +87,16 @@ "To illustrate the dynamics of the system, we create an input that correspond to driving down a curvy road. This trajectory will be used in future simulations as a reference trajectory for estimation and control." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "RMM notes, 27 Jun 2019:\n", - "* The figure below appears in Chapter 8 (output feedback) as Example 8.3, but I've put it here in the notebook since it is a good way to demonstrate the dynamics of the vehicle.\n", - "* In the book, this figure is created for the linear model and in a manner that I can't quite understand, since the linear model that is used is only for the lateral dynamics. The original file is `OutputFeedback/figures/steering_obs.m`.\n", - "* To create the figure here, I set the initial vehicle angle to be $\\theta(0) = 0.75$ rad and then used an input that gives a figure approximating Example 8.3 To create the lateral offset, I think subtracted the trajectory from the averaged straight line trajectory, shown as a dashed line in the $xy$ figure below.\n", - "* I find the approach that we used in the MATLAB version to be confusing, but I also think the method of creating the lateral error here is a hart to follow. We might instead consider choosing a trajectory that goes mainly vertically, with the 2D dynamics being the $x$, $\\theta$ dynamics instead of the $y$, $\\theta$ dynamics.\n", - "\n", - "KJA comments, 1 Jul 2019:\n", - "\n", - "0. I think we should point out that the reference point is typically the projection of the center of mass of the whole vehicle.\n", - "\n", - "1. The heading angle $\\theta$ must be marked in Figure 3.17b.\n", - "\n", - "2. I think it is useful to start with a curvy road that you have done here but then to specialized to a trajectory that is essentially horizontal, where $y$ is the deviation from the nominal horizontal $x$ axis. Assuming that $\\alpha$ and $\\theta$ are small we get the natural linearization of (3.26) $\\dot x = v$ and $\\dot y =v(\\alpha + \\theta)$\n", - "\n", - "RMM response, 16 Jul 2019:\n", - "* I've changed the trajectory to be about the horizontal axis, but I am ploting things vertically for better figure layout. This corresponds to what is done in Example 9.10 in the text, which I think looks OK.\n", - "\n", - "KJA response, 20 Jul 2019: Fig 8.6a is fine" - ] - }, { "cell_type": "code", "execution_count": 3, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -197,10 +173,10 @@ "Linearized system dynamics:\n", "\n", "A = [[0. 1.]\n", - " [0. 0.]]\n", + " [0. 0.]]\n", "\n", "B = [[0.5]\n", - " [1. ]]\n", + " [1. ]]\n", "\n", "C = [[1. 0.]]\n", "\n", @@ -253,20 +229,6 @@ "The unit step responses for the closed loop system for different values of the design parameters are shown below. The effect of $\\omega_c$ is shown on the left, which shows that the response speed increases with increasing $\\omega_\\text{c}$. All responses have overshoot less than 5% (15 cm), as indicated by the dashed lines. The settling times range from 30 to 60 normalized time units, which corresponds to about 3–6 s, and are limited by the acceptable lateral acceleration of the vehicle. The effect of $\\zeta_\\text{c}$ is shown on the right. The response speed and the overshoot increase with decreasing damping. Using these plots, we conclude that a reasonable design choice is $\\omega_\\text{c} = 0.07$ and $\\zeta_\\text{c} = 0.7$. " ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "RMM note, 27 Jun 2019: \n", - "* The design guidelines are for $v_0$ = 30 m/s (highway speeds) but most of the examples below are done at lower speed (typically 10 m/s). Also, the eigenvalue locations above are not the same ones that we use in the output feedback example below. We should probably make things more consistent.\n", - "\n", - "KJA comment, 1 Jul 2019: \n", - "* I am all for maikng it consist and choosing e.g. v0 = 30 m/s\n", - "\n", - "RMM comment, 17 Jul 2019:\n", - "* I've updated the examples below to use v0 = 30 m/s for everything except the forward/reverse example. This corresponds to ~105 kph (freeway speeds) and a reasonable bound for the steering angle to avoid slipping is 0.05 rad." - ] - }, { "cell_type": "code", "execution_count": 5, @@ -274,7 +236,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -374,19 +336,6 @@ "plt.tight_layout()" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "RMM notes, 17 Jul 2019\n", - "* These step responses are *very* slow. Note that the steering wheel angles are about 10X less than a resonable bound (0.05 rad at 30 m/s). A consequence of these low gains is that the tracking controller in Example 8.4 has to use a different set of gains. We could update, but the gains listed here have a rationale that we would have to update as well.\n", - "* Based on the discussion below, I think we should make $\\omega_\\text{c}$ range from 0.5 to 1 (10X faster).\n", - "\n", - "KJA response, 20 Jul 2019: Makes a lot of sense to make $\\omega_\\text{c}$ range from 0.5 to 1 (10X faster). The plots were still in the range 0.05 to 0.1 in the note you sent me.\n", - "\n", - "RMM response: 23 Jul 2019: Updated $\\omega_\\text{c}$ to 10X faster. Note that this makes size of the inputs for the step response quite large, but that is in part because a unit step in the desired position produces an (instantaneous) error of $b = 3$ m $\\implies$ quite a large error. A lateral error of 10 cm with $\\omega_c = 0.7$ would produce an (initial) input of 0.015 rad." - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -434,23 +383,6 @@ "A simulation of the observer for a vehicle driving on a curvy road is shown below. The first figure shows the trajectory of the vehicle on the road, as viewed from above. The response of the observer is shown on the right, where time is normalized to the vehicle length. We see that the observer error settles in about 4 vehicle lengths." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "RMM note, 27 Jun 2019:\n", - "* As an alternative, we can attempt to estimate the state of the full nonlinear system using a linear estimator. This system does not necessarily converge to zero since there will be errors in the nominal dynamics of the system for the linear estimator.\n", - "* The limits on the $x$ axis for the time plots are different to show the error over the entire trajectory.\n", - "* We should decide whether we want to keep the figure above or the one below for the text.\n", - "\n", - "KJA comment, 1 Jul 2019:\n", - "* I very much like your observation about the nonlinear system. I think it is a very good idea to use your new simulation\n", - "\n", - "RMM comment, 17 Jul 2019: plan to use this version in the text.\n", - "\n", - "KJA comment, 20 Jul 2019: I think this is a big improvement we show that an observer based on a linearized model works on a nonlinear simulation, If possible we could add a line telling why the linear model works and that this is standard procedure in control engineering.\t" - ] - }, { "cell_type": "code", "execution_count": 7, @@ -458,7 +390,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -517,28 +449,6 @@ "## Output Feedback Controller (Example 8.4)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "RMM note, 27 Jun 2019\n", - "* The feedback gains for the controller below are different that those computed in the eigenvalue placement example (from Ch 7), where an argument was given for the choice of the closed loop eigenvalues. Should we choose a single, consistent set of gains in both places?\n", - "* This plot does not quite match Example 8.4 because a different reference is being used for the laterial position.\n", - "* The transient in $\\delta$ is quiet large. This appears to be due to the error in $\\theta(0)$, which is initialized to zero intead of to `theta_curvy`.\n", - "\n", - "KJA comment, 1 Jul 2019:\n", - "1. The large initial errors dominate the plots.\n", - "\n", - "2. There is somehing funny happening at the end of the simulation, may be due to the small curvature at the end of the path?\n", - "\n", - "RMM comment, 17 Jul 2019:\n", - "* Updated to use the new trajectory\n", - "* We will have the issue that the gains here are different than the gains that we used in Chapter 7. I think that what we need to do is update the gains in Ch 7 (they are too sluggish, as noted above).\n", - "* Note that unlike the original example in the book, the errors do not converge to zero. This is because we are using pure state feedback (no feedforward) => the controller doesn't apply any input until there is an error.\n", - "\n", - "KJA comment, 20 Jul 2019: We may add that state feedback is a proportional controller which does not guarantee that the error goes to zero for example by changing the line \"The tracking error ...\" to \"The tracking error can be improved by adding integral action (Section7.4), later in this chapter \"Disturbance Modeling\" or feedforward (Section 8,5). Should we do an exercises? \t" - ] - }, { "cell_type": "code", "execution_count": 8, @@ -551,12 +461,12 @@ "output_type": "stream", "text": [ "K = [[0.49 0.7448]]\n", - "kf = [[0.49]]\n" + "kf = 0.4899999999999182\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -630,23 +540,6 @@ "To illustrate how we can use a two degree-of-freedom design to improve the performance of the system, consider the problem of steering a car to change lanes on a road. We use the non-normalized form of the dynamics, which were derived in Example 3.11." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "KJA comment, 1 Jul 2019:\n", - "1. I think the reference trajectory is too much curved in the end compare with Example 3.11\n", - "\n", - "In summary I think it is OK to change the reference trajectories but we should make sure that the curvature is less than $\\rho=600 m$ not to have too high acceleratarion.\n", - "\n", - "RMM response, 16 Jul 2019:\n", - "* Not sure if the comment about the trajectory being too curved is referring to this example. The steering angles (and hence radius of curvature/acceleration) are quite low. ??\n", - "\n", - "KJA response, 20 Jul 2019: You are right the curvature is not too small. We could add the sentence \"The small deviations can be eliminated by adding feedback.\"\n", - "\n", - "RMM response, 23 Jul 2019: I think the small deviation you are referring to is in the velocity trace. This occurs because I gave a fixed endpoint in time and so the velocity had to be adjusted to hit that exact point at that time. This doesn't show up in the book, so it won't be a problem ($\\implies$ no additional explanation required)." - ] - }, { "cell_type": "code", "execution_count": 9, @@ -704,6 +597,55 @@ "vehicle_flat = fs.FlatSystem(vehicle_flat_forward, vehicle_flat_reverse, inputs=2, states=3)" ] }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "# Utility function to plot lane change trajectory\n", + "def plot_vehicle_lanechange(traj):\n", + " # Create the trajectory\n", + " t = np.linspace(0, Tf, 100)\n", + " x, u = traj.eval(t)\n", + "\n", + " # Configure matplotlib plots to be a bit bigger and optimize layout\n", + " plt.figure(figsize=[9, 4.5])\n", + "\n", + " # Plot the trajectory in xy coordinate\n", + " plt.subplot(1, 4, 2)\n", + " plt.plot(x[1], x[0])\n", + " plt.xlabel('y [m]')\n", + " plt.ylabel('x [m]')\n", + "\n", + " # Add lane lines and scale the axis\n", + " plt.plot([-4, -4], [0, x[0, -1]], 'k-', linewidth=1)\n", + " plt.plot([0, 0], [0, x[0, -1]], 'k--', linewidth=1)\n", + " plt.plot([4, 4], [0, x[0, -1]], 'k-', linewidth=1)\n", + " plt.axis([-10, 10, -5, x[0, -1] + 5])\n", + "\n", + " # Time traces of the state and input\n", + " plt.subplot(2, 4, 3)\n", + " plt.plot(t, x[1])\n", + " plt.ylabel('y [m]')\n", + "\n", + " plt.subplot(2, 4, 4)\n", + " plt.plot(t, x[2])\n", + " plt.ylabel('theta [rad]')\n", + "\n", + " plt.subplot(2, 4, 7)\n", + " plt.plot(t, u[0])\n", + " plt.xlabel('Time t [sec]')\n", + " plt.ylabel('v [m/s]')\n", + " # plt.axis([0, t[-1], u0[0] - 1, uf[0] + 1])\n", + "\n", + " plt.subplot(2, 4, 8)\n", + " plt.plot(t, u[1]);\n", + " plt.xlabel('Time t [sec]')\n", + " plt.ylabel('$\\delta$ [rad]')\n", + " plt.tight_layout()" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -713,14 +655,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -738,88 +680,92 @@ "Tf = xf[0] / uf[0]\n", "\n", "# Define a set of basis functions to use for the trajectories\n", - "poly = fs.PolyFamily(6)\n", + "poly = fs.PolyFamily(8)\n", "\n", "# Find a trajectory between the initial condition and the final condition\n", - "traj = fs.point_to_point(vehicle_flat, x0, u0, xf, uf, Tf, basis=poly)\n", - "\n", - "# Create the trajectory\n", - "t = np.linspace(0, Tf, 100)\n", - "x, u = traj.eval(t)\n", - "\n", - "# Configure matplotlib plots to be a bit bigger and optimize layout\n", - "plt.figure(figsize=[9, 4.5])\n", - "\n", - "# Plot the trajectory in xy coordinate\n", - "plt.subplot(1, 4, 2)\n", - "plt.plot(x[1], x[0])\n", - "plt.xlabel('y [m]')\n", - "plt.ylabel('x [m]')\n", - "\n", - "# Add lane lines and scale the axis\n", - "plt.plot([-4, -4], [0, x[0, -1]], 'k-', linewidth=1)\n", - "plt.plot([0, 0], [0, x[0, -1]], 'k--', linewidth=1)\n", - "plt.plot([4, 4], [0, x[0, -1]], 'k-', linewidth=1)\n", - "plt.axis([-10, 10, -5, x[0, -1] + 5])\n", - "\n", - "# Time traces of the state and input\n", - "plt.subplot(2, 4, 3)\n", - "plt.plot(t, x[1])\n", - "plt.ylabel('y [m]')\n", - "\n", - "plt.subplot(2, 4, 4)\n", - "plt.plot(t, x[2])\n", - "plt.ylabel('theta [rad]')\n", - "\n", - "plt.subplot(2, 4, 7)\n", - "plt.plot(t, u[0])\n", - "plt.xlabel('Time t [sec]')\n", - "plt.ylabel('v [m/s]')\n", - "plt.axis([0, Tf, u0[0] - 1, uf[0] +1])\n", - "\n", - "plt.subplot(2, 4, 8)\n", - "plt.plot(t, u[1]);\n", - "plt.xlabel('Time t [sec]')\n", - "plt.ylabel('$\\delta$ [rad]')\n", - "plt.tight_layout()" + "traj1 = fs.point_to_point(vehicle_flat, Tf, x0, u0, xf, uf, basis=poly) \n", + "plot_vehicle_lanechange(traj1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## Vehicle transfer functions for forward and reverse driving (Example 10.11)\n", - "\n", - "The vehicle steering model has different properties depending on whether we are driving forward or in reverse. The figures below show step responses from steering angle to lateral translation for a the linearized model when driving forward (dashed) and reverse (solid). In this simulation we have added an extra pole with the time constant $T=0.1$ to approximately account for the dynamics in the steering system.\n", - "\n", - "With rear-wheel steering the center of mass first moves in the wrong direction and the overall response with rear-wheel steering is significantly delayed compared with that for front-wheel steering. (b) Frequency response for driving forward (dashed) and reverse (solid). Notice that the gain curves are identical, but the phase curve for driving in reverse has non-minimum phase." + "### Change of basis function" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "bezier = fs.BezierFamily(8)\n", + "traj2 = fs.point_to_point(vehicle_flat, Tf, x0, u0, xf, uf, basis=bezier)\n", + "plot_vehicle_lanechange(traj2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "RMM note, 27 Jun 2019:\n", - "* I cannot recreate the figures in Example 10.11. Since we are looking at the lateral *velocity*, there is a differentiator in the output and this takes the step function and creates an offset at $t = 0$ (intead of a smooth curve).\n", - "* The transfer functions are also different, and I don't quite understand why. Need to spend a bit more time on this one.\n", - "\n", - "KJA comment, 1 Jul 2019: The reason why you cannot recreate figures i Example 10.11 is because the caption in figure is wrong, sorry my fault, the y-axis should be lateral position not lateral velocity. The approximate expression for the transfer functions\n", - "\n", - "$$\n", - "G_{y\\delta}=\\frac{av_0s+v_0^2}{bs} = \\frac{1.5 s + 1}{3s^2}=\\frac{0.5s + 0.33}{s}\n", - "$$\n", - "\n", - "are quite close to the values that you get numerically\n", - "\n", - "In this case I think it is useful to have v=1 m/s because we do not drive to fast backwards.\n", - "\n", - "RMM response, 17 Jul 2019\n", - "* Updated figures below use the same parameters as the running example (the current text uses different parameters)\n", - "* Following the material in the text, a pole is added at s = -1 to approximate the dynamics of the steering system. This is not strictly needed, so we could decide to take it out (and update the text)\n", + "### Added cost function" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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F9gCPqeouEfm0iHzaOuxpvGP2B/F+mPhMsO+3dn4BAM/uqp1I2I6oa+uhurWH5SFYyjaS61dNZW9tO9srW8c/2A//9cxeYmOEf798ftDnMEndRr6k7s8YS1FRkdPhGFFmSUkG8Z4YysO06IUxonnDl62p6k5g2URPqqpPq+ppqjpLVb9jPfYrVf2V9b2q6met5xeratBraKdkJLJsaibP7a4b/+AQ23qsBYBl0zJdef+rlhWRGBfDI28dm/C5th1v4ekdtdx6zkwK0oMfSjBJ3UaJcR7SEmI50TF+S72mJuCeMGOSS4zzsLgkI2wrWRkj2iMi94rIeSLyPhG5B2/rOqKcNzePHVWttHb5V4cjVN6ubCHOIywoTHfl/dMT47h6aTFPbKum1c8aJaP572f3kZMSz63nzpzQeUxSt1luWgIN7aZGt+GMstIsdlS1+r3Fr+G6m4FdwBeAL+JdS36zmwEF4/QZOagSdh8o3z7ewvzC9JAUnRnNx86YTnf/IH/YXBn0Od44dILXDp7gM+fPJjVhYuVjTFK3WV5aAg1+dL+vWBHVVRsNh6yank3/oPL28Ra3QzH8oKo9qvpjVf2Adfuxqva4HVeglk/LJN4Tw5thtKRyaEjZXtnK0pJMV+NYVJxB2fQsHnj9CANBrOdXVf772X1MSU/kH06fNuF4TFK3WV5aAif8aKlv3rw5BNEY0cZXYKP8qJksFwlEZI6I/EFEdovIYd/N7bgClRjnYdnUTN48Ej4t9cMnOujoHQhJvffxfOqcmVQ2d/NMEJMJ/7a3ni3HWvj8hXNs6XEwSd1meakJ1PuR1NetWxeCaIxok5USz6y8FDabpB4pHsBbd30AOB94GPi1qxEFqaw0i93VbWEz9LPtuHfG+dKSDJcj8a7nn5mbwv/+7WBAW7IODin/9cxeZuSmcF1ZiS2xmKRus/z0BDp6B+jqG7tU7D333BOiiAw7jLcrViitnJ7FlmPNYbufs/EuSar6IiCqelRV7wCCrSTnqqVTMxkYUnZVt7kdCgDbK1tIifcwMy/V7VDwxAifu2A2e2vbA1r69/vy4+yv6+BfLp5LnE37hYRil7ZJxVfVqL6tl9Jc898bCiJyauGNkTSp6ieCPL8/u2KFTNn0bB4rr+TwiQ5m56e5EYLhvx4RiQEOiMjngCog3+WYgrLM6uZ++3hLSOusj2Z7ZSuLijPwhMkuclctLeIXLx/iv57Zy4XzC4iPHTtJt3b18/1n97GqNIvLFk+xLQ6TdWxWkJ4AQH17L6W5KS5HM2nMBz41xvOCNykHy59dsULGV+O6vKLZJPXw90UgGfg88G28XfA3uRlQsArSE5mSnsjblS1uh0L/4BC7a9pCujPbeGI9MXzj8vnc/MAm7n3tMJ85b/aYx3/n6d20dvdz+5ULEbHvg4lJ6jbztdTr2sae4FpV5czuPpPUN1T1lbEOEJE7J3D+kXa8es/e1CKyDlgHMG3axGexjmZWXgqZyXFsOdbMDaudex9jYqweng+r6leADiJwKduplpRkhMXKi/117fQNDLHY5ZnvpzrvtDwuWVjAT144wEXzCzitYOQP3c/tquWx8ko+c94sFhXbOyfAjKnbLD/tnZb6WMzsd/uo6mN2HDOGCe94ZScRYeW0LDNZLsyp6iCwUuxshrlsUXEGFY1dtPe4W4Rmh1WWdYnNCXGiRIT/vGYx6YmxrHu4nOYRSobvqWnjy4+9zZKSDL5w0RzbYzBJ3WaZyXHEe2Kobx+7pX7VVVeFKKLJQ0TKRORxEdkiIttFZIeIbLfh1LbseGWnFdOzONTQaTZ3CX9bgSdE5GMicq3v5nZQwVpY5K3ctre23dU4tle1kp4Yy/ScZFfjGEleWgK/+uhKqlt6uP7uDRw50XnyudcOnOAj92wkNTGWX350JQmx9hfNMd3vNhMRbwGaNlNVzgW/Bb4C7AACrwIxupO7YuGd6HQD8BEbzx8w30SlLceauWBegZuhGGPLBhp594x3Bf7kTjgTs7DI2zLeVdXKqtJs1+LYWeWdJBeunSBlpdk8ePMqbvvNZi7+8SuUTc+mo3eAHVWtzMpL4b6bVlGcmeTIe5uk7gB/q8oZtmtQVX9mwgdEVQesmcvPAh7gflXdZff7BGJJiXfW75ajLSaphzFVjfhx9OEK0hPITY13dVlb38AQe2vaufnsUtdi8MeZs3N58cvv455XD/PWkSaS4jz822Xz+NiaUpLinStra5K6A/LTEjja2DXmMXfddVeIoplUbheRe4EXgZOfqlR1wq0iVX0a73aWYSE5PpYFhelsOWbG1cORiKxT1bsneky4EREWFGW4mtT317XTNzjE4jAbTx9Jfloi37h8QUjf0yR1B+SlJYxbxtNUlHPEzcA8II53ut8jtqtzPCumZfL7zZUMDA4Ra1PhCsM2XxeRE2M8L3g3eYmopA4wvzCNBw410j84ZFvBlEDsqPJOkouEpO4Gk9QdkJuaQFNn35gXvYigaiqC2Wypqi52O4hQWTE9i4c2HGVfXfvJsU4jbLwCXDnOMc+HIhC7zZ+STt/gEEdOdI66ZMtJO6xJctOyw2+SXDgwSd0BudaytubOPvInsNm9EbCNIrLArUpvobZimjVZ7mizSephJtrG0oebV+hN5Htq2lxJ6uE+Sc5tjvadiEimtUPRXhHZIyJniEi2iDwvIgesr+7XG7RZXmo8ACc6zHKjEDsb2GbVaLdzSVtYKslKIj8tgS3HWtwOxZhEZuWlEucR9tSEflmbb5Kc6XofndMt9Z8Cz6jqh0QkHm+5xH8DXlTV71kbY3wd+JrDcYRUdoq3pd7YOfoM+CuuuCJU4Uwml7odQCiJCCunmyI0RmjFeWKYnZ/GnprQT5bzTZJbaJL6qBxrqYtIOnAucB+AqvapagvemtkPWYc9BFzjVAxuyU7xttSbRqgm5PPkk0+GKpxJw9oF6z03t+Ny0oppWRxr6qLBj+1+DcMu86aksc+FAjQ7zSS5cTnZ/T4TaAAeEJGtInKviKQABapaA2B9HXHHIhFZJyLlIlLe0NDgYJj28yepX3nleHNoDH+JyBY7jolEvs1dTGs9fInI5SLyVRH5pu/mdkwTNXdKGrVtPbR2hbZc7M7qVtISYpluJsmNysmkHgusAH6pqsuBTrxd7X4JVR1tJ2QkxQHQMsYF/9RTT4UqnMlgvjWGPtptB5DrdpBOWFScTrwnhq1mvXpYEpFfAdcD/4R3Gdt1QPhsLRakuVO8E+T21YW2tb6jqo2FxenEhMl2q+HIyTH1SqBSVd+07v8Bb1KvE5FCVa0RkUKg3sEYXOGJEdITY2ntdnfTg0lknh/HDDoehQsSYj0sLskwLfXwdaaqLhGR7ap6p4j8kAnUTRCRbOB3QClQgXcXuPf88EXkfuAKoF5VFwX7fqOZ50vqtW2snhGacrH9g0PsqWnj42si/jORoxxrqatqLXBcROZaD12Id//p9byzn/BNwBNOxeCmjOQ4s9lGiIw2ln7KrdLtOJ2ycnoW26ta6R2Iys8tka7b+tolIkVAPzBjAuf7Ot6JxnPwVk4crffzQRycODolPZG0xNiQbuxysL7D2m7VjKePxelyQP8E/NZaVrQM+C7wPWCtiBwA1lr3o05aQhztPQOjPm8Kzxh2WTEti76BIXZWuVe60xjVUyKSCfwA2IK3df3oBM7n10RjVX0VaJrA+4xJREI+Wc5XSc7UZBibo0ldVbdZ4+JLVPUaVW1W1UZVvVBV51hfHbvw3JSWGDtmUr/77oirDmmEqZUnJ8tF5a9SpPu+qrao6h/xjqXPA/5zAufza6KxvyYyIXnulDT21bWHrIGys6qVlHgPM3NTQvJ+kcoUjHZIakIsHb2jJ/XbbrsthNFMDiLyuWgsZjSevLQEpuckU15hxtXD0AbfN6raq6qtwx8biYi8ICI7R7hdbXdwE5mQPLcgjfaeAWrbeuwOa0Q7qlpZWJRhJsmNw5SJdUhKQiydfaMndcMRU4BN1vK1+4FndZKMc6ycnsUr+xpQVVM+MwyIyBSgGEgSkeV4Z74DpOMtwjUqVb1ojPOGzUTjuVPSAdhb205hhjN7g/sMWJPkPrLaTJIbj2mpOyQ53kN3n5m4FEqq+u/AHLwFjz4BHBCR74rILFcDC4Gy6dk0dvZRMc6Wv0bIXAL8N1AC/Aj4oXX7Z7xVNYMVNhON51p13/eHYFz9YEMHPf1DLC5Jd/y9Ip1J6g5JjPPQ0z96Ul+/fn0Io5k8rJZ5rXUbALKAP4jI910NzGFlpd5Rh/IKM64eDlT1IVU9H/iEqp4/7Ha1qk5kK+ARJxqLSJGIPO07SEQewdvNP1dEKkXklgm854gykuOYkp4Yksly2ytNJTl/me53hyTExdAzMDTq8ytXrgxhNJODiHweb+vlBHAv8BVV7ReRGOAA8FU343PS7LxUMpPj2FTRxHVlU90Ox3jH6yJyH1Ckqu8XkQXAGap6XzAnU9VGvMuDT328Grhs2P0bgw04EKdNSQvJsrZ3JsmlOv5ekc601B0S74lhYHD0pF5cXBzCaCaNXOBaVb1EVX+vqv0AqjqEtxBH1IqJEcqmZ5nJcuHnAeBZoMi6vx/4omvR2GzelDQONnSM+bfODtsrvdutmkly4zNJ3SFxnhiGFMcvduMdqvrN0TZwUdU9oY4n1FaVZnP4RCcnOszmLmEkV1UfA4YAVHWAKKpuOG9KGn0DQ1Q0djr2Hv2DQ+yuaTNd734ySd0hHusT5eDkmHxthIFVVrnOTUfMuHoY6RSRHEABRGQN0OpuSPbx1YB3sgt+f127qSQXAJPUHRJjLSsaLaffeuutIYzGmAwWFWWQFOfhTZPUw8mX8M5YnyUirwMP4620GRVm56fiiRH21jiX1H2T5JaUZDr2HtHETJRziG+p8NAoWd1UlDPsFh8bw4rpmbxlknrYUNUtIvI+YC7eter7fHM9okFCrIcZuSmOttS3V7aQnhhLaY7ZbtUfpqXuEF8yjxmlEIiZ/W44YXVpDntq28wOgeFlNbAU71bUN4rIx12Ox1bzpqSxt9a5fQfePt7K0qmZpqiSn0xSd4ivgT7adbhly5bQBWNMGqtnZKMaWePqA4NDtHb3MzgUffNPROTXeIvQnA2ssm5lrgZls/mF6VQ2d9PWY/8HyZ7+QfbVtbPEjKf7zXS/O6TfmvUeF2M+Nxmhs3xaJvGxMWw83MhFCwrcDmdUPf2D/L78OH/aWsXOqlb6BxVPjLCoKJ3LlxRy/appZCTFuR2mHcqABdFcrnhBoVUutqbd9r3Vd1W3MjikZjw9ACapO2RgUIkRRl1XWVhYGOKIjMkgMc7DimmZbDjc6HYoo3r94Am++oftVLV0s7AonU+ePYO81AROdPSx8XAj3316L//7t4P8yyVz+ejp0yN9bfJOvHsS1LgdiFPm+5J6bZvtSX3rsRYAlk/NtPW80cwkdYf0DgySEOsZ9fnq6uoQRmNMJmtm5vDTFw/Q2tVPRnJ4tXbvefUw3/3rHmbkpvB/nzqdM2fnvueYnVWtfO+ve/nmE7t4fncdP7l+GTmpCS5EGzwReRLvMrY0YLeIvAWcLCCgqle5FZvdCtITyEyOY0+N/ePqb1e2UpSRSH56ou3njlamb9ghPf1DJMaN/t97xx13hC4YY1I5Y2YOqrDxSHi11n/8/H6+8/QeLl04haf+6ewREzrAouIMfn3Lar77gcW8eaSJq3/+OgfrnS9FarP/xruByx3ANcB3eWdTlx+6FpUDRIQFhensrrY/qW873syyaZm2nzeamaTukO7+QZLiRm+p33nnnSGMxphMlk/LIinOwxsHT7gdykkPvn6En754gOtWlvC/H1lBcvzYnYQiwkdOn8Zjt51BT/8QH/zlBrYdbwlNsDZQ1VdU9RXgMt/3wx9zOz67LSxKZ09t+8m5RHZo7OjleFM3S814ekBMUndIZ+8AKQlmdMMIvfjYGFbPyOa1MEnqfz/QwLee2s3aBQV874NLTlZb9MeyqZn86R/PJCMpjo/e+2Yk7kK3doTH3h/yKBy2sCiDvoEhDjV02HbOk+Pp07JsO+dkYJK6QzpMUjdcdPbsXA41dFLb2uNqHLWtPXz+ka3MyU/jpzcsCyih+0zLSeb3nz6D/LQEbrr/LTYfDf/ELiL/KCI78G59un3Y7Qiw3e347LawyDtZbleVfV3wm481ExsjZjlbgExSd0hbdz/pYyzJKS8vD2E0xmRz9hzvePXfDzS4FsPQkPLPv9tG78AQv/zo+F3uYylIT+SRdWvIT0/kpvs3RUJX/P8BV+ItEXvlsNtKVf2om4E5YWZeKolxMeyycVx9y9FmFhZnkDjGMKbxXiapO6S1uz9a1tkaEWjelDTy0hJ4Zb97Sf3BNyrYcLiR269cwMy8ie+DXZCeyP/dejrZKfF8/L432VkVvvuiqGqrqlao6o2qenTYLfy7GYLgiRHmF6bb9jPpHxzi7coWVphJcgEzSd0hLd39ZI6R1MvKoqqolBFmRIRz5uTy2sETrlRqO97Uxfef3csF8/L5cNlU285bmJHE/916OmmJcfzDveGd2CebJcUZ7LSKxUzU7uo2evqHWGHG0wNmkroDfGUvs1Pi3Q7FmMTed1oeLV39bK9sCen7qirf+PNOPCJ85wOLbK/ZXZKVzKPr1pCaEMtH7tnI5qPNtp7fCM6Skky6+gZtmSy3yZoQaXcxm8nA0aQuIhUiskNEtolIufVYtog8LyIHrK9R91GspbsfVUxSN1x17pw8YgRe2lsf0vf9685aXt3fwL9cMpfCjCRH3mNqdjK/u20N2SnxfPTeN3lhd50j72P4b+lU74S2t22Y77Cpoolp2ckUmKIzAQtFS/18VV2mqr7+5q8DL6rqHOBF635UaWj3Fo7KSxu9Ctbtt98eqnCMSSorJZ4V07J4MYRJvatvgG8/tZsFhel8bM10R9+rJCuZ33/6TGbnp3Lrr8v5+UsHGYrCTWEixczcVFITYk/ufx4sVaW8oplVpaaVHgw3ut+vBh6yvn8Ib7WlqFLvR1I3FeUih4j8QET2WkuSHheRTLdj8tcF8/PZVd0WsqVtv3z5EDWtPXzr6oXEepz/85KXlsDvblvDFUuK+MGz+/jIvRttXStt+C/GWn420ZUJhxo6aOzsY/WMqOvEDQmnf+sUeE5ENovIOuuxAlWtAbC+5o/0QhFZJyLlIlLe0ODeDN5g1LV5/4AWpI3edVRUVBSqcIyJex5YpKpLgP3Av7ocj9/Wzvfu1Pb87lrH3+t4Uxd3vXqYq5cVURbCVlZyfCw/u2EZ//XBxeyqbuPiH7/Klx7bxltHmqJyO9dwtnJ6Frtr2ujqGwj6HBsOecsbnzFz5DLCxticro5ylqpWi0g+8LyI7PX3hap6N3A3QFlZWUT9ZtZZraL89NFb6jU1UbtpU9RR1eeG3d0IfMitWAI1Oz+VmbkpPLurjo+dUeroe/3XM3uJEfjapfMcfZ+RiAjXr5rGBfMK+PlLB3ms/Dh/2lJFSryH2QVp5KXGkxDrIS8tgTuuWhjy+OwiItnA74BSoAL4sKo2n3LMVOBhvLvDDQF3q+pPQxHfiulZDA4pbx9v5YxZOUGd441DjRRnJjE125n5GNHO0Za6qlZbX+uBx4HVQJ2IFAJYX0M7iycEqlt7yEmJN0UTotMngb+O9EQ49i6JCBcvnMLGw420dPU59j6bjzbz1PYa1p0zk6JM9/4Y+5L2W9+4iP+5cTkfXFlCWkIs1S097K1t42B9xHfN+zMnaQD4sqrOB9YAnxWRBaEIbsVUb5f5lmPBrUgYGlI2Hm7kjFk5tq+amCwca6mLSAoQo6rt1vcXA9/CW2HpJuB71tcnnIrBLVUt3RRnjf2HbcWKFSGKxvCHiLyAt2Vzqm+o6hPWMd/A+wfztyOdI1x7ly5bPIVfvXKIZ3fVcv2qabafX1X5z7/sJj8tgdveN8v28wcjNSGWK5cWceXSqBvmuho4z/r+IeBl4GvDD7CGNX1DnO0isgcoBnY7HVxGchxz8lNPLkkL1O6aNpq7+jljZnCtfMPZ7vcC4HHr01Ys8H+q+oyIbAIeE5FbgGPAdQ7G4Iqq5i7m5KeNeczmzZtDFI3hD1W9aKznReQm4ArgQlUNm4Ttj8XFGUzPSeap7TWOJPWnttew9VgL3//gErPfgfPeNSfJGtoclYiUAsuBN0d5fh2wDmDaNHuujdNnZvP4lioGBocCniz5qlXW+JzTzHh6sBzrflfVw6q61LotVNXvWI83quqFqjrH+hpVZROHhpTK5u5xx4PWrVs35vNG+BCRS/G2hq5S1S634wmUiHDlkiJeP3ji5HJLu/T0D/Jfz+xlfmE6H1xZYuu5JysReUFEdo5wuzrA86QCfwS+qKojFmVX1btVtUxVy/Ly8uwInzUzc+jsG2RHENX+XtnXwILCdPLHmGRsjM1UlLNZQ0cvvQNDTMtOHvO4e+65J0QRGTb4XyAN72TPbSLyK7cDCtQ1y4sYUnhiW5Wt573/9SNUNnfzH5fPD2oHNuO9VPUiVV00wu0J/JyTJCJxeBP6b1X1T6GL3pvUATYcbgzode09/Ww+2sy5p9nz4WKyMkndZkcbvQ25qeMkdSNyqOpsVZ1qFVFapqqfdjumQM3OT2PZ1Ex+X16JXaMH9W09/PxvB7lofgFnzjbdpSHim5MEo8xJEu+Y533AHlX9UQhjAyA3NYG5BWm8duBEQK97ZX8DA0PK+XNNUp8Ik9RtVtHYCcCM3BSXIzGMd/vQyhL21bXbtm3p95/dR9/gEN+4fL4t5zP88j1grYgcANZa9xGRIhF52jrmLOBjwAVWz9I2EbkslEGeNzePTRVNdPT6v179uV11ZKfEh7TGQTQySd1mRxs7iY2RcZf1VFXZ2w1qGOO5ZnkxqQmx/HrD0Qmfa8uxZv6wuZJPnjXDfIANodHmJKlqtapeZn3/mqqKqi4Z1rv09Nhnttd5c/PpH1ReP+hfa71vYIiX9tVz4bx8M4wzQSap2+xwQyfTspOJG2fWp5n9boRaakIs164o5qntNROaMDcwOMR//HknBekJ/NOFc2yM0IgWZaVZpCXE8uIe/zbaeXV/A+09A1y6aKRVpUYgTFK32eGGTr9aLldddVUIojGMd7v5rBn0Dw3xwOtHgj7Hg29UsKu6jW9esZBUs4TNGEGcJ4YL5+fz3O46+gaGxj3+z9uqyEqOM5PkbGCSuo0Gh5QjjZ3Myk91OxTDGNGM3BQuW1TIrzccDarCXMWJTv77uX1cMC+fyxabVpUxuiuXFtHS1T9uF3xbTz8v7KnjiiVF4/ZwGuMz/4M2qmzuom9giNl5Jqkb4etzF8ymo2+AX758KKDXDQwO8ZU/vE2cJ4bvfmCxKeNpjOmcOXmkJ8by+Nax5w/9aXMlPf1DXFdm6hzYwSR1G/nqSs/KH7/7/a677nI6HMMY0fzCdD6wvJgH3qjgyIlOv1/3s78dZFNFM9++ehFTMkxxEGNs8bExXLuihL/uHH0Ox9CQ8vDGoyybmsmSkszQBhilTFK30QErqc/OG7tELJiKcoa7vnbpPBJiY/jXP21nyI/tSV/YXcfPXjzAB1eUcM3y4hBEaESDj50xnf5B5bdvjrzi4q87aznc0MnNZ5WGNrAoZpK6jQ7UdZCXlkBGcty4x5quS8NNBemJ/MflC9h4uImfvnhgzGM3H23ic49sYUlJBt/5wKIQRWhEg1l5qaxdUMC9fz9CY8e7W+t9A0P86Pl9zMlP5YolUbfxjmtMUrfRgfp2Tisw4+lGZLiurIQPrijhpy8e4KE3KkY85m976/jYfW9RmJHEfTetMtsJGwH72qXz6O4f5D//sudd1Qx/8sJ+DjV08q+XzTNr021k1qPYZGhIOVDXwfWrprodimH4RUT4f9cuprW7n9vX72LLsWY+c95sZuSmcPhEBw+8VsHvyo+zoDCdB29eRV5agtshGxFodn4q/3TBbH7ywgEK0hO57dyZPLrpOL94+RAfLivhgnkFbocYVUxSt0lVSzfd/YOcVjD+eDrAFVdc4XBEhjG++NgYfvXRFfzsxQP86tXDPLGt+p3nPDF86uwZ/Mslc00L3ZiQz18wh5qWHn71yiF+9Yp31cWlC6fwravNcI7dTFK3yf66dgDmTvGv+/3JJ590MhzD8FusJ4YvXTyXj54xnZf3NlDb1sOU9ETOm5dntsA0bBETI/zXh5Zw9fIidlS2sqAonbNm5RJjut1tZ5K6TfbXWTPf8/1rqV955ZUmsRthJT8tkQ+b4SPDQWfOyuXMWWZHPyeZiXI22V/XTmFGIhlJ4898B3jqqaccjsgwDMOYbExSt8n+una/x9MNwzAMwwkmqdtgcEg5UN9hlrMZhmEYrjJj6jY42thJ38BQQC314es1jeizefPmEyJyahmtXMC/DaZDbzLHNt3Bc0cMc83ayrVr1iR1G/gmyc0JIKnffffdplRsFFPV9+whKSLlqlrmRjzjMbEZ5pq1j5uxme53Gxys9y5nmxPAlqu33XabU+EYhmEYk9SYLXURWe/HOZpU9RNjnMMDlANVqnqFiGQDvwNKgQrgw6ra7G/A4ehAfQfFmUmkJJiOD8MwDMM942Wh+cCnxnhegJ+Pc44vAHuAdOv+14EXVfV7IvJ16/7X/Ig1bB2o62B2AK10Y9K62+0AxmBiM0YSzv/3JrYRjJfUv6Gqr4x1gIjcOcZzJcDlwHeAL1kPXw2cZ33/EPAyEZzUh4aUwyc6OGNWTkCvW7/en04QI5qoatj+ETKxGSMJ5/97E9vIxhxTV9XHxjvBOMf8BPgqMDTssQJVrbFeWwPkj/RCEVknIuUiUt7Q0DBeGK6paummp3+IWXmBtdRXrlzpUESGYRjGZOXXRDkRKRORx0Vki4hsF5EdIrJ9nNdcAdSr6uZgAlPVu1W1TFXL8vLeMykzbBw+0QnArLyUgF5XXFzsRDiGYRjGJObv7PffAg8AHwSuBK6wvo7lLOAqEakAHgUuEJHfAHUiUghgfa0PIu6wcbjBu5xtZoAtdWPyEJFLRWSfiBy05pGEBRGZKiIvicgeEdklIl9wO6ZTiYhHRLaKiKmrHGLmug2O29esv0m9QVXXq+oRVT3qu431AlX9V1UtUdVS4Abgb6r6UWA9cJN12E3AE8EGHw4qTnSSlhBLbmq826EYYcha/fFz4P3AAuBGEVngblQnDQBfVtX5wBrgs2EUm49voq0RQua6nRBXr1l/k/rtInKviNwoItf6bkG+5/eAtSJyAFhr3Y9YRxq7mJ6bjEhgWwjeeuutDkVkhJnVwEFVPayqfXh7ra52OSbAO6dFVbdY37fj/UMUNuNCwyba3ut2LJOQuW6DEA7XrL8Lq28G5gFxvDPpTYE/+fNiVX0Z7yx3VLURuDCQIMPZ8aYuFhSmj3/gKe6+O2wnbhr2KgaOD7tfCZzuUiyjEpFSYDnwpsuhDPcTvBNtzU5JoWeu2+D8BJevWX+T+lJVXexoJBFocEipbO7i0kVTAn7typUr2bw5qDmERmQZqQsnrAr/i0gq8Efgi6ra5nY88O6JtiJynsvhTEbmug08nrC4Zv3tft8YZmMWYaG2rYf+QWVqVnLAr92yZYsDERlhqBKYOux+CVDtUizvISJxeP8w/lZV/ep5C5HRJtoaoWGu28CFxTXrb1I/G9hmzYT0a0nbZFDV3A1AcVaSy5EYYWwTMEdEZohIPN5Jo2FReUi8E0HuA/ao6o/cjme4MSbaGqFhrtsAhcs162/3+6WORhGhqluspJ4ZeFIvLCy0OxwjDKnqgIh8DngW8AD3q+oul8PyOQv4GLBDRLZZj/2bqj7tXkhGODDXbeTyK6mPt3xtsqpp7QGgMCMx4NdWV4dNT5bhMOuPTdj9wVHV1xh57DSsDJ9oa4SOuW6D5+Y1O2b3u4iMO/DrzzHRqq6th7SE2KB2Z7vjjjvsD8gwDMOY1MYbU59vjaGPdtsB5IYi0HBU395DXnpCUK+9885R98ExDMMwjKCM18Sc58c5Bu0IJBKd6OgjNzW4pG4YhmEYdhszqZux9LE1dvQyd4qpi2EYhmGEB3+XtBkjaO3uJzM5uJrv5eXlNkdjGIZhTHYmqQdJVWnp6iczKc7tUAzDMAwD8H8/9fdUk5vspRt7+ocYGFJSEwOf+Q5QVlZmc0SGYRjGZOdvS/0xEfmaeCWJyP8A/8/JwMJdR+8AAKlBLGczDMMwDCf4m9RPx1sH+A285QOr8Vb1mbS6+7yT/pPjTVI3DMMwwoO/Sb0f6AaSgETgiKoOjf2S6NY74E3qCbHBTUu4/fbb7QzHMAzDMPxO6pvwJvVVeDd3uVFE/uBYVBGgd8D7mSY+yKRuKsoZhmEYdvO37/gWVfWtwaoFrhaRjzkUU0QYUu/Wwh4JrgRxUVGRqf8exXJzc7W0tNTtMAw/bN68+YSq5rkdh9vMNRs5xrpm/d3Q5T2LqlX11xMNLBoEmdOpqamxNxAjrJSWlppaBBFCREyRLcw1G0nGumbNOvUgibVJ0JC6HIhhGIZhWExSD1Ksx5vUB4eCmy+4YsUKO8MxDMMwDJPUgxVnJfW+weCa6ps3b7YzHGMSUlW2HmtmYHBSL0QxJglVZV9tO68fPEGnVSfEeC+T1IOUGOcBoKcvuE3q1q1bZ2c4xiTTOzDIzQ9u4gO/eIOv/nE7qmYcyIheTZ19fOKBTVzyk1f5h3vfZNV3XuCPmyvdDissOZbURSRRRN4SkbdFZJeI3Gk9ni0iz4vIAetrllMxOCnFKjrT2RfcJ8Z77rnHznCMSebpHTW8vK+Bc0/L409bqvjrzlq3QzIMR3T2DvDx+99k4+FG/u2yeTx48yqWlGTw5d+/ze82HXM7vLDjZEu9F7hAVZcCy4BLRWQN8HXgRVWdA7xo3Y84vprvHT2mG8gIvUfeOs70nGTuv6mM/LQEHt9a5XZIhuGI/3pmL7uq2/jlR1ew7txZnDc3n9/ccjrnzMnlG4/vZGdVq9shhhXHkrp6dVh346ybAlcDD1mPPwRc41QMTorzxJAS76Glu9/tUAw/iMj9IlIvIjuHPXaHiFSJyDbrdtkIrxuxx8lNVS3dvHWkiQ+XTSXWE8OVS4t4ZV8DrV3mWjSiy9vHW3h4w1E+cWYpF8wrOPl4rCeG/7lxOZnJcfzb4zsYNMuQTnJ0TF1EPCKyDagHnlfVN4ECVa0BsL7mOxmDkzKT42nq7AvqtVVVpmUVYg8Cl47w+I9VdZl1e3qE50frcXLNW0caATh/rvdX5/IlhfQNDvHKgQY3wzIM2/34hf1kJcfx5Yvnvue5zOR4/v3yBWyvbOXJt00hLx9Hk7qqDqrqMqAEWC0ii/x9rYisE5FyESlvaAjPP1Z5aQk0tPcG9Voz+z20VPVVoCmI143W4+Sa8opm0hJimTslDYDFxRkkxXnYcrTZzbAMw1Y7q1p5eV8Dt547c9TdMK9aWsS8KWn89MUDZhWIJSSz31W1BXgZb0upTkQKAayv9aO85m5VLVPVsry88KzgWJCeQH17T1Cvveqqq2yOxgjS50Rku9U9P+KkzVF6nEY6LiQfRDcfbWbZtEw8Md5llXGeGJZOzWDLMZPUjejx8IYKkuM9fHTN9FGPiYkRvnjRHI6c6OS53XUhjC58OTn7PU9EMq3vk4CLgL3AeuAm67CbgCecisFphRlJVLf0mOVEkeuXwCy83eo1wA9HOsjfHqdQfBBt6+lnX107ZdOz3/X4yulZ7KpuoyvI1RiGEU5au/p5Yls11ywvJj0xbsxj1y6YwtTsJB54/UiIogtvTrbUC4GXRGQ73l3enlfVp4DvAWtF5ACw1rofkUqykujoHaDVTJaLSKpaZyXsIeAeYPU4x7fwTo+TK/bXtqMKi0vS3/X4yulZDA4pOyrNTOBoIiKXisg+ETkoIu9ZKSReP7Oe3y4iK4Y9VyEiO6xJoBFV1P0vO2roHRjixlXTxj3WEyPcdEYpmyqa2VPTFoLowpuTs9+3q+pyVV2iqotU9VvW442qeqGqzrG+BjzOGS6mZScDcLSxK+DX3nXXXXaHYwTINwxk+QCwc4RjRutxcsX+Ou/w/pz8tHc9vrAoA4B9de0hj8lwhoh4gJ8D7wcW4N3yesEph70fmGPd1uHtfRrufGsSaJnT8drpiW1VzMpLYVFx+vgHAx9cUUK8J4bHyo87HFn4MxXlJmBmXgoAh090jHPke5mKcqElIo8AG4C5IlIpIrcA37daMtuB84F/to4tEhHfTPjRepxccaC+naQ4D8WZSe96PD8tgYykOPbVmqQeRVYDB1X1sKr2AY/iXRI83NXAw9aEzo1A5ikfViNObWsPb1U0cfWyYsTPbTCzUuJZu7CAx7dW0TsQXJXPaOHvfurGCKZmJ+OJEQ7Vdwb8WhExY/EhpKo3jvDwfaMcWw1cZn2/HVjuYGgBOVjfwZyCVGJi3v3HTkSYW5DGftNSjybFwPCmZyVwuh/HFOOdI6LAcyKiwF2qevepbyAi6/C28Jk2bfyu7lB4fnctqnDZ4sA+m3xoZQl/2V7Dq/tPsHZBwfgviFKmpT4BCbEeZuamsNe0jowQ2V/Xzuz81BGfO21KKvtq282HxegxUjP11B/uWMecpaor8HbRf1ZEzn3PgWG4yui53XXMzE0Z9Tofzdmzc8lKjuOJbZO7BohJ6hM0rzDdTM4wQqK1u5+6tt73jKf7nFaQRlvPAHVtwdVOMMJOJTB12P0S4NQqK6MeY/U4oar1wOOMMxE0HLT19LPxcGNQLe04TwyXLynkhT11k3oXN5PUJ2hhUTpVLd00B1hZ7oorrnAoIiNaHbMmZM7ITRnxeV+yN13wUWMTMEdEZohIPHAD3iXBw60HPm7Ngl8DtKpqjYikiEgagIikABczwkTQcPPGwRP0DyoXzAuu0Ojli4vo6R/ilf3hWbAsFExSn6Alxd5ZxzsC3FTgySefdCIcI4oda/Imdd+qi1P5Jm4ebQx8jocRflR1APgc8CywB3hMVXeJyKdF5NPWYU8Dh4GDeJdlfsZ6vAB4TUTeBt4C/qKqz4T0HxCEV/afIDUhlhXTg9u8c/WMbHJS4if1roVmotwELSrJQAS2Hmvh3NP8H5O68sorTWI3AnK82ZvUp2Ynjfh8floCSXEejpwIfImlEZ6s/QiePuWxXw37XoHPjvC6w8BSxwO0kary6v4GzpyVQ5wnuPamJ0a4eGEB67dV09M/SGKcx+Yow59pqU9QemIccwvSKD8a2HL7p55ybVWUEaGONXWRnRJP2igVtkSE6TnJVJiWuhGBjpzopKqlO6DG0UguXjiFzr5BNh5utCmyyGKSug3KSrPYeqzFbChgOOp4UxdTs0ZupfvMyE0xSd2ISG8c8ibhs2bnTug8Z8zMITnewwt7JmcteJPUbbBmZg4dvQMBj6sbRiCONXUxdZTxdJ/pOSkcb+oyHzCNiLPxcCMF6QmU5ox9jY8nMc7DOXNyeWF3/aRc3mmSug3WzMwB3vmk6Y/JeLEZwRscUqqau0edJOczIzeZ/kGluiW43QMNww2qysbDTZwxM8fvKnJjuXB+AbVtPeyehMuNTVK3QW5qAvML03k1gGUUd9/9nuJOhjGq2rYeBoaUkqyxk/q0bGsGfJPpgjcix6GGTk509J5sIE3UeXO94/Iv75t8S9tMUrfJeXPz2Hy0mfYe/3Zsu+222xyOyIgmNS3dABRmJo55nG9mfGVzt+MxGYZdyiu8E41Xz8ge50j/5Kclsrg4g5f21ttyvkhikrpN3ndaHgNDymsHTrgdihGFqlu93elFGWNPlJuSnkhsjHC8ySxrMyLHWxVN5KTEj1pYKRjnz8tny7FmWroCKwwW6UxSt0nZ9CwykuJ4fpLOuDSc5WupF43TUo/1xFCYmWha6kZEKa9opqw0y5bxdJ/3nZbHkMJrBydXQ8skdZvEemK4YF4+f9tb79fM4/XrT632aBijq2ntIS0hdtQ16sNNzUqmstm01I3IUN/ew7GmLsqm29P17rO0JIOMpDhemWTj6iap2+iShVNo6epn4+HxC9GsXLkyBBEZ0aK6pXvc8XSfkqwkjpuWuhEhthxtAQi6NOxoYj0xnD07l1cPNEyq1UYmqdvovLl5JMd7+MuOmnGPLS4uDkFERrSoae2hcJzxdJ+pWck0tPfS0z/ocFSGMXFbjzcT5xEWFqXbfu5zT8ulrq2X/XUdtp87XJmkbqPEOA8Xzi/gmZ019JviH4aNalq7xx1P9ykxM+CNCLL1WAsLizIcqdN+zhzv0ra/H5g8XfAmqdvs6qVFNHf1m1nwhm16BwY50dHnd0vdt5a9qsUkdSO8DQwOsaOyleXTMh05f1FmErPzUyfVVqwmqdvs3NPyyEiK48/bqsY87tZbbw1RREakq2/rBWBKhn8t9aJMb/KvNkndCHP76trp7h9k2dRMx97jnDm5vHWkadIMR5mkbrP42BiuXFrIs7tqxyxEYyrKGf6qa/OuUS9I9y+pF6QlECMmqRvhb3uld78Mp5N678AQ5RXNjr3HRNS39fCPv9nMym8/z3/8eSe9AxP78GGSugOuXVFCT/8QT48xYc7Mfjf8VWe11AvSE/w6PtYTw5T0RNP9boS9t4+3kJkcN+6eBhNx+owc4jzC3w+GXxd8Z+8ANz+4iZf3NbByeha/3niUHzyzb0LndCypi8hUEXlJRPaIyC4R+YL1eLaIPC8iB6yv9q5jCAPLp2YyKy+Fx8orRz1my5YtIYzIiGQnW+pp/rXUwdsFH8qW+kt76/nvZ/exIYBNjQzj7cpWlpRk2lp05lQpCbEsn5YVlvOc/udvB9lV3cYvPrqCuz9exsfPmM69rx1hX2170Od0sqU+AHxZVecDa4DPisgC4OvAi6o6B3jRuh9VRITrV01l89FmDtYH/8MxDIC69h7iPTFkJo9feMbHm9RDs1PbvX8/zM0PbuJ/XzrIR+7dyK83VITkfY3I1t03yP66dpaWZDj+XufMzmVXdRuNHb2Ov5e/jjd1cf9rR/jgihLOn5sPwBcvOo14TwyPvHUs6PM6ltRVtUZVt1jftwN7gGLgauAh67CHgGucisFN164oITZGePSt4yM+X1hYGOKIjEhV39ZLfnpCQK2Zoswkalq7GRpytujGwfoOvvfXvVy8oIC3b7+Y807L49tP7eFAnfkwa4xtT20bg0PKomLnk/rZc3KBwLbHdtp9rx1BUb5yydyTj2WnxHPpoin8cUtl0BP7QjKmLiKlwHLgTaBAVWvAm/iB/FFes05EykWkvKEh/MZCxpObmsDFCwtG/eFUV1e7ENXkJSL3i0i9iOwc9tgdIlIlItus22UjvG7EYaRQqm3t8XuSnE9xVhL9g0qDwy2T7z+zl6Q4D9+9djEZSXF8/0NLSU7w8N2n9zj6vkbk21nlnSS3OARJfXFxBmmJsWHTBd/a1c9j5ce5amnxe1a1fGB5Me09A2yqGL8y6UgcT+oikgr8Efiiqvq9Y72q3q2qZapalpeX51yADrpx9TSau/p5Zmfte5674447Qh/Q5PYgcOkIj/9YVZdZt6dHeH60YaSQqWvvYUqASb3I+kPh5GS5mtZuXthTx8fPnE5uqncSX15aAjefOYOX9jWw37TWjTHsqGwlJyWeQj+Xak5ErCeGM2fl8NrBE2FRMvbP26ro6hvk5rNK3/Pc6hnZxHkk6I1oHE3qIhKHN6H/VlX/ZD1cJyKF1vOFQNRueHvWrFxKc5L5zcaj73nuzjvvdCGiyUtVXwUC/ug7xjBSyPi63wPhK1RT4+C4+h83VzKk8OGyqe96/ONnTCcxLoYHXq9w7L2NyLejqpVFxRmOTpIb7uw5eVS1dFPR6P5mR7/ffJyFRekjDj34Jva9Hm5JXbw/qfuAPar6o2FPrQdusr6/CXjCqRjcFhMjfHTNdMqPNrO72u9OCmMUInKtH7f3dKGP43Mist3qnh9zJcYpw0gjPW/7kFFn7wAdvQPkBzDzHd7ZorWm1bmW+hPbqlk9I5vpOe/eAzsrJZ73Lyrkqe3Vk6bghxGYnv5BDtR3hKTr3efs2d5xdbe3Yt1b28bOqjauW1ky6jFnWxP7gtkL3smW+lnAx4ALThmz/B6wVkQOAGut+1HrQytLSIyL4dcbK9wOJRrcA1wBXDnG7X8CON8vgVnAMqAG+OFoB/ozjOTEkFF9u3dMPD8tsJZ6RlIcSXEex2bAH2vs4kB9B5cunDLi875xwZf3RV9HnLUsd7xbpttxhrN9te0MDqkjm7iMpjQnmeLMJF5zuQ78+m3VeGKEK5cWjXrM8mmZqMKuIBqDsRMJbiyq+howWr/KhU69b7jJTI7nmmXFPL61iq9fOp8Ma1lSeXm5y5FFpL+q6ifHOkBEfuPvyVS1btjr7gGeGuWcIw0jhUSDL6kH2P0uIhRmJjrWUv/bXu9/3QXzRpznypmzcshNTeDJ7TVcuijqVnpUW7ex+o09wLTQhBN5fMlqYVHoWuoiwtmzc3l6Zw0Dg0PEekJfe01V+cuOGs6clUNO6ui/077/l51VrZxl9TD4y1SUC4GPn1FKT/8Qj5WPvLzN8I+qftSOY3x8czssHwB2jnDMaMNIIVHf7m1pB9r9DlCUkUR1qzMt9Zf2NTAzN4XS3JQRn4/1xLB2QT6v7GuYcNnLMLRHVWeq6ozRbkD4rJ0KQ7uqW0lLjGVqtn+bFNnlnNNyae8ZYLs18z7UdlW3cbSxiyuWjP1BNzslnqKMRHYG0VI3ST0EFhSls7o0m4c3VjBorRsuKytzOarIJSLXiUia9f2/i8ifRGTFOK95BNgAzBWRShG5Bfi+iOwQke3A+cA/W8cWiYhvJvxow0gh4Wup5wXY/Q5QmJFIjQOz3wcGh9hU0XRy7e9oLppfQEfvABsPB7c0J4ydYdMxk9au6jYWFKaHbJKcz1mzchHBtaVtz+ysJUZg7YKRh62GW1icwa7qwD98ONb9brzbJ84q5TO/3cKLe+q4eJRxSMNv/6GqvxeRs4FLgP/GOz5++mgvUNUbR3j4vlGOrQYus74faxjJcfXtvcR5hMwk/6vJ+RRmJtHQ0UvfwBDxsfZ9ft9V3UZX3yCrZ2SPedxZs3NJivPwwu463ndaZC5LHcVnxkpGqvojVQ1NOb8INDik7K1t48bVoR+dyEqJZ1FRBn8/0MDnL5wT8vd/dlctp8/IITslftxjFxVl8MKeOjp7B0hJ8D9Vm5Z6iFy8oICijESzzMcevv7cy4FfquoTwPi/JRGovq2X3NQEYmIC/1xRlJGI6ju14+3iK4qxunTspJ4Y5+GMWTn83eWJSQ5Is25lwD/iXeJYDHwaCGkNg0h05EQnPf1DLCgM3SS54c6Zk8vWYy1j7qLphCMnOjlQ38ElCwv8On5OQSqq3tcFwiT1EIn1xPCxM0rZcLiRPTVt3H777W6HFMmqROQu4MPA0yKSQJReyw0dvQHPfPcptPZVr7U5qb95pInSnGTy/SiIc+6cXCoauzgWBmuD7aKqd6rqnUAusEJVv6yqXwZWAqOvUzIA2FPjHSdeEMKZ78OdMyePgSEN+bDQC7u9k0svWuBfUp9hzVcxST2M3bh6KolxMTz4eoWpKDcxHwaeBS5V1RYgG/iKqxE5pL6th7wgJskBJyt12blbm6qy9VgLK6b7t7niuVa3+yvR11oH7+z24QuJ+4BSd0KJHLtr2oiNEWbnp7ry/iumZ5Ic7+HV/aG9Jp/fU8e8KWmUZPm3zaxJ6hEgMzmea1eU8OdtVUwpHH2NojEyq7DLT4FzgadV9QCcrPr2nLvROeNER29Qk+TgnaReY+MM+JrWHk509LJsaqZfx8/ITaE4M4nXw6Tmts1+Dbxl7SFwO96iRA/b+QYicqmI7BORgyLynh0txetn1vPbh08YHe+1btlT08bs/FQSYj2uvH9CrIc1M3N4NYQfNFu6+th8tJm1frbSwTt8VZyZZJJ6uLv5zFJ6B4aoq61xO5RItAZ4HDgPeEVEnhaRL4jIae6G5YyBwSEaO/uCTuppiXGkJcTaOgP+7eMtACwtyfTreBHhzFk5bDjc6PiOcaGmqt8BbgaagRbgZlX9rl3nFxEP8HPg/XjH6m8cYd+B9wNzrNs6vBNG/X2tK/bUtLk2nu7zvtPyONrYRUWACTNYL+9rYHBIR63rMJoZuSkcNkk9vM0pSOMcaylQ/+CQy9FEFlUdUNWXVfXrqno6cAvQDvyniGwVkV+4HKKtGjv7UA28mtxwhZmJtq5V31bZQpxHmFeY5vdrzpydQ2t3P7trorJU8hG8SyW3Amkicq6N514NHFTVw6raBzyKd+vq4a4GHlavjUCmVX/Bn9eOqbyiiUcnsK/3SBo7eqlr6w3o+nGCbzVGqFrrf9tbT25qvN8fhn1m5KZwpKEjoE1oTFJ3wSfOLCW+YNaIu7cZ/rO63e9X1Q/jnaT0W7djslNDkCVihyvMSKLWxqS+/Xgr8wvTA+o6PXOW90NssBtUhCsR+RTwKt75HXdaX++w8S2KgeEVqyp572ZCox3jz2vH3K/grztr+fZTu4OPfgT7ar079813uaVempvC9JxkXt7nfFIfGBzi5X31nD83P+BVLDNyU2jrGaCp0/8a8Capu+C8ufmc/qV7eHhDhduhRCQRKRORx0VkizWOuB3Ypqqvux2bnSZSeManMMO+UrGqyq7q1oBLexakJzIzL4U3j0RdEZovAKuAo6p6Pt7NfuzMEiNlgFObbKMd489rx9yvICMpjs6+QVt7FPeESVIHb2t9w6FGxzcd2ny0mbaegYC73gE+VFbCtm+uHbOk7KlMUneBJ0bgtbvYVNEcVMUgg98CDwAf5N2buUQVe5J6Eic6+mwp1VrV0k1bz0BQm3CsmZnDW0eaGIiuIaceX5EZEUlQ1b3AXBvPXwkM39e2BG/NeX+O8ee1Y8qwCh61ddu3nntvTRu5qQnkBpCknHL+3Hy6+wd5y+EPm3/bW0+cRzgniAJM6YlxZCYHVoLDJHWX/P3J33l3b9vw3r3WjXE1qOp6VT2iqkd9N7eDsltDhzepT+QPYKG1BasdXfC+TTiCWV+8ZmYOHb0DQe06FcYqrd3Y/gw8LyJPEGDiHMcmYI6IzBCReOAGvFtXD7ce+Lg1C34N0KqqNX6+dky+pN5qY1LfU9vGfJfH033WzMwhITaGlxzeSfDFvfWcPiOH1ACqwk2ESeouumZZMX/eVkVrV2grG0WB20XkXhG5cfhe6m4HZbf6th7SE2NJjAt+6U+xVYDGji1Yd1e3ESMwf0oQSd0qKbvhcHTsc2Jt9PN5VW1R1TuA/8Bbdvgau95DVQeAz+Edq98DPKaqu0Tk0yLyaeuwp4HDwEG8WxN/ZqzXBvL+dif1gcEh9td1MLcgPJJ6Ury34uHf9tYHNBEtEMcauzhY38H5QXS9B8skdRd97Izp9PQP8YctlW6HEmluxrsH+qW80/V+hZsBOaFhAmvUfewsQLO7po0ZuSkkxQf+ISM/PZFZeSm8GSVJXb1Z4M/D7r9i9R75P6PJv/d5WlVPU9VZ1hI6VPVXqvorXxyq+lnr+cWqWj7WawORbnNSr2jsom9gKCzG030unJfP0cYuDjU4s7TthT1WFbn5JqlHvaqqKhYWZbBsaib/9+ZRxz4pRqml1uSem1T1Zus25j7rkaihfeJJvchqqdsxWW5vbduE/iCvmZlDeUVzNI2rbxSRVW4H4RS7W+p7a71DL3OnhEdLHeCC+d5iMC9aydduL+6tY05+KtNzRt6i2Akmqbtk8+bNAPzD6dM41NAZjTODnbQxXAppOMmb1IMrEeuTGOchJyWeqgl2v3f0DnC8qZt5E/iDvGZmDu29A9G0Xv18YIOIHLJWYfi28Y0Kdk+U21vTjsfF8rAjKc5MYn5h+skWtZ3aevp583ATF873v4qcHUxSd8lVV10FwBVLikhLiOV3m46P8wpjmLOBbVYJzKj7Y+rT0N5Lng2zhAszJ76szbe+eG4Q4+k+p8+0xtUPRUcXPN5qbbOAC3hnCChqVmHY31JvZ0ZuyoTmiDhh7YICNh9t5oQ1MdUuL+2tZ2BIWbsgdF3vYJK665LiPVy9vIind9SYCXP+uxRvWcyLicI/pgCdvQN09g1OuPsdoCgjacJj6r6u04m01PPTvOPq0TJZbvjKi2hchREfG0NSnMfW7vdw6nr3uWRhAUNqfxf8c7vryE1NYNlU/zY/sotJ6mHg+rJp9A4M8eR2O1fDRK9o/2MK9lST8ynKTKJmgt3v+2rbSU2IpSQraULnOWNWDpuONEV0iWQR2WLHMZEgIymOFhsaGx29A1Q2dzMvTGa+D7egMJ3izCSe3WVfUu8dGOSVfQ2sXZDvrUsSQiapu+Suu+46+f2i4nROK0jlj2YW/Jgm0x/Tel9ST7cjqSfS3jtAW0/wf5z31rYzpyAV70qu4J0xM5fOvkF2VEV00aX5vkqGo9x24N1rPeJlJMXZ0lL3Dd/MC6OZ7z4iwvsXTeG1Aycm9Dsy3OsHT9DRO8DFC6bYcr5AhGY1vPEe69atO/m9iPDBFSX8v7/u5ciJzpP76BrvMX+csXMBAqthGqbsqCbn45sBX9XcTXphXMCvV1X217Xz/kUT/wO1Zti4+oppoe2WtNE8P45xtvZoiNie1MOw+x3gsiWF3PvaEV7YXce1K0omfL6nd9SSlhjLWbND/9nOsZa6iNwvIvUisnPYY9ki8ryIHLC+Ruxv9USd2uK5alkRIrB+m+mCH8M83l0W9tTbFcCZrkVno/p2b3d5/gRnv8M7BWiqmoMbV2/o6KWlq5/TbOg6zUlNYH5hekRv7jLa8M8pt6jodku3Lam3kRLvOXkthpvlUzMpykjkqe0T3xK7f3CI53fXsXZ+AfGxoe8Md/IdH8Q7oWm4rwMvquoc4EXrvoG3RvfpM7J54u0qs2Z9FJPpj2l9ey+xMUJmUuAt61OVZCUD3trtwdhf2wFgWyWws2d716t390VFYzaqZSTF2bKkbW9tO6dNSQt4l7JQERGuXFrEq/sbAtoRbSSvHThBa3c/ly0utCm6wDiW1FX1VeDUxddXAw9Z3z+EjSUVo8GVS4s43NDJ/roOt0MxXOYrPGPHH8Hc1HgSYmOCTur76rxdp6fZ1HV61uxc+gaH2FRhajOEu8zkOFommNRVlX117WHb9e5zzfJiBoaUv0xwwvIT26rISIrj3CA2cLFDqPsGCqzNBrC+hnYBXxi54or3VjW9eMEUROAvOybeBWREtnobqsn5iAjFmUlUNncF9fr9te3kpMTbtrPW6hnZxHti+PsB5/eydoKIfFJEEqzvrxaR20QkKoZ9TpWdEk9X3+CEtietb/cO34RLzffRzC9MZ96UNP64pSroc3T1DfDc7jouWzzFla53COPZ7yKyTkTKRaS8oSEyf/nH8uSTT77nsby0BFaVZvPcrloXIoocIvLPIjLx2SxhrKG915blbD7FWUlBj6nvq/POfLdLcnwsq2dk8/K+iP29/oKq9orIHcCXgBl4Nxl6Q0RCP93ZQdkp3m0/J9IlvdeGwkWh8qGVJWw73nJyYl+g/rqjlq6+QT6w3L0/T6FO6nUiUghgfR11zztVvduq712Wl+dON4aTrrxy5FopF83PZ29te9CtqkkiHXhWRP4uIp8VkXHrMI4ycfMOEakSkW3W7TJ/X+u0hvaeCZeIHa4kKymo7ndV5UBdu+2trPPm5nGgviPoIQGX+TLcZcAFqvp1Vb0E+A7wC/fCsp8dSX1/mM98H+7aFSXEeYRHNx0L6vWPlR+nNCeZVaXuzQEPdVJfD9xkfX8T8ESI3z9sPPXUUyM+7qsT/NJeZ/f4jWSqeqeqLgQ+CxQBr4jIC+O87EHeO3ET4Mequsy6PR3gax3RPzhEY2efbd3v4J0Bf6KjL+DJaVUt3XT2Ddo2nu5z3lzvB/UIvc6Pi8iDeIcPT07nVtW/4G21Rw27Wur5aQlkWecKZ9kp8VyycAp/2FxJZ+9AQK891NDBm0eauK5s6oTrOUyEk0vaHgE2AHNFpFJEbgG+B6wVkQPAWuu+MczM3BSmZifxyv7IXfITQvVALdDIOPMzRpm46ZeJvDYYJzp6UYUp6Xa21L0z4APtAdpvTZKzu6U+Ky+V6TnJPL/bmd2xHPYJ4BW8E3//aA0HXSwiX+OdVnxU8CX15q7g/1n76sKzPOxobj6rlPaeAf60NbCx9YffqCDeE8P1q6Y6FJl/nJz9fqOqFqpqnKqWqOp9qtqoqheq6hzrq5n+egoR4Zw5eWw4dCKiS2k6SUT+UURexrssMhe4VVWXBHm6z1lVwO6faN0Eu+aB1LV5C88U2FBNzmdajjepH2sKNKl7V2LMsTmpiwgXLyhgw6FG2m2q4hUqqtqmqg+o6tvAdXiLeH0CmAZc72ZsdstO9ib1xo7gkvrA4BAH6jrCfpLccCumZbG0JIP7/n7Y722CW7v7+cPmSi5fUmjbhNJghe1EuWg31lr0s2ZFRSlNJ00HvqiqC1X1dlXdHeR5fol3l61lQA3ww4kEZdc8kLo2b+GZAhtb6tOyg0zqte1MSU88uWOXndYumELf4FAkT5jzJfgfqOpHVPWzqlrhdkx2ykiKI0aCb6lXNHbROzAUluVhRyMi/ON5s6lo7PK7GM1Db1TQ2TfIp85xf/TFJHWX3H333aM+t3qGt5Tmm4dNR8ZIrIlJ22w4T52qDqrqEHAPsHrCwdmg3krqdtR998lJiSc53sPRxsCS+t7adse6TldOzyIvLYGnzEZGYSsmRshKjg96TD3cy8OO5uIFBcybksaPX9hP78DY81Bau/q577UjXDQ/n4VF7lepNkndJbfddtuoz+WlJTAzL4VyU5zDUb6VGJYPACGb3T6WurZePDFCbop9SV1EmJadzPEAWuoDg0McbOhw7A+yJ0a4fHEhL+1rsG0jDcN+2SkTSeptxAjMzrdvSWQoxMQI/3bZfI42dvHA6xVjHvuTF/fT3tPPl9bODU1w4zBJPUwtn5rFtuMtpmSsTUaZuPl9EdlhbRJzPvDP1rFFIvL0OK91TF1bD/k2VZMbblp2ckDd7xWNnfQNDDk6yemqZUX0DQzxzA5TmyFcZU0gqe+pbWdGbgqJcR6bo3LeuaflsXZBAT9+fj8H60det775aDMPbzjK9aumsaAoPIYYTFIPU8umZdLY2UdlkAVDjHcbZeLmx1R1saouUdWrhlU7rFbVy8Z6rZOx1rX3km/jeLqPL6kPDfn3QfGdoiHOJfXlUzOZlZfC78qPO/YexsTkTKil3s68CCg6M5rvfGARKQmx3PbrzTSf8n9Q19bD5x/ZSmFGIv96mT8b94WGSeouWb9+/ZjPL7I+9e2uaQtFOEYYqWvtocDGNeo+03OS6R0Yos7aAW48+2rb8cQIs/Kc6zoVEW5YNY3NR5tPLp8zwkt2SjyNQST1jt4BjjV1Rdx4+nD5aYn86qMrOd7czXV3beBtq/e0vKKJG+7eSEtXHz//yArSE+2fSBosk9RdsnLlyjGfnzslDRHYY5L6pFPX3mPrzHefGbne5HykodOv4/fUhKbr9IMrS0iIjeGB1484+j5GcPLTEmnq7KNvILAltr5JcvMjaOb7SFbPyObBm1fR3NnH1T9/nXn/8Qwf+tUGuvsGeeiTq1k6NdPtEN8l1u0AJqvi4uIxx8uT42OZlp3MgXqzY9tk0t03SEtXP1My7E/qM/NSADh0opMzZ+eOe/yemjZWTne+3GV2SjwfXFnCHzZX8s9rT7NlD3nDPr5VGCc6eikKYD90X4NkXmHkttR9zpyVy0tfOY8ntlVzrLGTmXmpXL6kMKxa6D6mpR7GZuamcNjPVpURHWpavXMoCh1I6lPSE0mO93C4YfwPii1dfVS1dIds8s+t58xkYHCIX718OCTvZ/jPt7FQfXtvQK/bW9tGWmIsxQF8EAhn6YlxfGzNdL5x+QJuXD0tLBM6mKQe1mbmpVJxotPMgJ9Ealq9492FGfb/IYyJEWbkpnDIjw+KvrkcC0LUdTojN4XrVk7lNxuPBrTsznCer+fEVxTJX3tr2pk/Jd3VOuiTkUnqLrn11lvHPaYkK4nu/sEJbaZgRJZqa9eyokxnuqBn5qX61VLfXe1N6qEcD/3i2jnEeoTb1+8yH2TDiK9ccSAt9aEhZU9NW1R0vUcak9RdMlZFOR9ft1WEbk9pBMHXUndiTB1gVl4KVS3d9PSPXSVrd3Ub+WkJtu4UN57CjCS+fPFc/ra3nj9srgzZ+xpjy0lNIEagIYCW+rGmLjr7BlkYJmu3JxOT1F0y3ux3eKf2t2+DDyP61bR2k5saT0KsMzPO5+SnoQoH6sZurb9d2cKSkkxHYhjLJ84sZc3MbL75xC6z8iNMeGKEnNSEgP4OvTN8437Z1MnGJHWXbNmyZdxjfK2kEx0mqU8WVS09joyn+8y3ukPHSphtPf0cauhkaUno/yB7YoSf3bCc9KRYPvngppMTBw135aclUO9nfQPw9vR4YoQ5BZFVHjYamKQexnJSfdsemqQ+WdS0dDsy891nek4KyfGeMYsa7az07g7o1vrb/PRE7v/EKjp6BvjIPW8GPEHLsF9BemJAY+q7qluZnZcakeVhI51J6i4pLCwc95iEWA+JcTG0dpvNLiYDVaWmtSegtcCB8sQIc6ekjdlS31bZAsASF1rqPguLMnjg5lXUt/Vww90bTYvdZQXpCSfne/hjV3Vb2NRCn2xMUndJdbV/201mJMXR1j3gcDRGOGju6qejd4Cp1t7nTplfmM6emrZRZ5hvOdrMjNwUMpPjHY1jPGWl2Tx8y2oa2nu5/q7JldhFJFtEnheRA9bXEasAicilIrJPRA6KyNeHPX6HiFSJyDbrdtlIr/dXSVYyTZ19dPWN/7eovq2H+vZeFhWb8XQ3mKTukjvuuMOv41LiY+n04xfJiHy+HdSmOZzUFxVl0NYzMOLe6oNDyltHmjh9RrajMfhr5fRsfn3Lapo7+/jIPW/SEGABlAj2deBFVZ0DvGjdfxcR8QA/B94PLABuFJEFww75saous25Pn/r6QJRkWStx/NhgakeVd/hmsUnqrjBJ3SV33nmnX8clxXvo7ht7+ZERHUKV1MtKvY2+TRVN73lub20bbT0DnD4zPJI6wPJpWTz4yVXUtHZzy0Ob/GotRoGrgYes7x8CrhnhmNXAQVU9rKp9wKPW62xXkuW9Jv3ZNXJHVSsimOVsLjFJPcwlxnnoGwxsIwUjMh0PUVKfnZdKRlLciEl942HvY6fPyHE0hkCtnJ7N/964gh1VrXzlD9snQ3GagmFbAdcA+SMcUwwM37O20nrM53Misl1E7h+j+36diJSLSHlDQ8OowUy1WuqVzeNX+9tZ1crM3BRSEszWIm4wST3MxXmE3gB3RzIi09HGTvLSEkiKd3bGcEyMsKo0i/KK5vc89/K+embkpjg6WS9YFy0o4KuXzOMv22t48I0Kt8OZMBF5QUR2jnDzt7U9Uv1V36edXwKzgGVADfDDkU6gqnerapmqluXl5Y36RrmpCcTHxozbUldV3q5sdaXGgeFlPkq5pLy83K/j4jwxdPROiu7GSe9YU5fjrXSf1TOyeWFPPZXNXSe7Vlu7+9lwqJFbzp4RkhiC8en3zaS8oon/9/ReTp+RE9EzrFX1otGeE5E6ESlU1RoRKQTqRzisEpg67H4JUG2du27Yue4BnppIrDExQklm0rhJvbq1h4b2XpZPy5zI2xkTYFrqYU5EiP6eRgPgeFN3yJL6pQu9Syr/sr3m5GMv7a1nYEi5ZNGUkMQQDBHhB9ctJSM5ji89to3egaidb7IeuMn6/ibgiRGO2QTMEZEZIhIP3GC9DuuDgM8HgJ0TDag4K4nj43S/bz3m7f1ZPtX5LXuNkbmS1EdbhjGZlJWV+XVcjMCQyepRr7N3gKqWbmbkpoTk/ablJLOkJIOnhiX1/3vzGMWZSSwL867T7JR4vnftYvbWtvPzlw65HY5TvgesFZEDwFrrPiJSJCJPA6jqAPA54FlgD/CYqu6yXv99EdkhItuB84F/nmhApTkpHGkYe9fIrcdaSIiNMRu5uCjk3e/DlmGsxdt9tElE1qvq7lDHEgnMpoWTw8F6by320wpC98fwmmXFfOup3byyv4HE2BjeqmjijisXEBMT/lfdhfMLuHZ5Mb946SCXLpwS0d3wI1HVRuDCER6vBi4bdv9p4D3L1VT1Y3bHdFpBKu29A9S2jV7KeOuxZhYVZxDnMZ3AbnHjfz5kyzAMI1Lsr2sHvH84Q+Uf1kxjZm4K//L7t/nMb7dQkJ7A9aumhez9J+qbVy4gMzmOr/1xOwNmhYjj5lgfOPePshlQV98AO6paWVUaPsshJyM3kvp4yzAA/5daRKrbb7/dr+NKspKZnhOaLlnDPQfqO4iPjQnpzzoh1sNPb1jO9OxkijKTeOTWNY7PvLdTZnI8d161iB1Vrdz16mG3w4l6vl6kA9YH0FNtOdpC/6CGVY2DyciN2e9jLcN45wHVu4G7AcrKylwdVHZiTay/FeW+fc2igM47CdbvRqX9de3MykvFE+Ku78UlGfzhH88M6Xva6fIlhfxlxxR++sIBzpubx8IiU8XMKdkp8eSmxp/sVTrVm0ca8cQIZdPNJDk3udFSH3UZhmFMVvtr20Pa9R5N/vOaxWQmx/GFR7eZ5Z8Om5Ofxr5Rut83HGpkUVE6aYlxIY7KGM6NpD7qMgzDcIpVVateRHYOe8yvTS+cXq1R09pNdWsPS8N81nm4yk6J5yfXL+PIiU6++Og2M77uoMUlGeypbqOn/91LCVu6+thyrJlzTxu9gI0RGiFP6uMswzAMpzwIXDrC42NueuHHphl+ae3qZ2ho5KGRTVZlNzPBKHhnzs7l9isX8MKeOj73f1tHbbEPjvIzMPyzujSbvsEhth1vedfjr+xvYEjhgnkjVbM1QsmVinKjLcMwDKeo6qsiUhrES0+u1gAQEd9qjYCWYH7hd1upa+vl9isXsGbmu+uql1c0kRLvYb5Z2zshHz+jlP5B5Tt/2c3aH7Vw7Ypi8lITqGzuZtvxFg42dJCbmsALX3qf26FGrFWl2YjApiNN77qOX9hTT05KvOltCgNmMaEx2Y236YVfqzVg9BUbqspVS4vo7B3gkw9uYm9t27te9+bhJlZMzyLWrO2dsFvOnsHvbjuD0pwUfvHyIe54cje/3ngUBS5fXMj1ZVPHPYcxuozkOOYWpLHxSOPJx9p6+nl+dy0XL5wSETUOop2p/W5MZr8Evo139cW38W568clTjvFrtQaMvmJDRLh2RQlnz87liv95jc/+dgt/+fw5JMZ52F3dxr66dj68yiQbu6wqzeaRdWvo6R+ko3eArOT4kK8qiGYXzMvnV68coqa1m8KMJJ7YWkVP/xA3rjbXcDgwTQNj0lLVOlUdVNUh4B68Xe2nsm21Rn56Ij/88FIONXTy/57eA8Cjm44R74nh2uUjNv6NCUiM85CbmmASus1uWDUNBR596zjdfYPc8/cjLCxKZ3GxWU4YDkxL3Zi0fLtgWXdH2/Ti5GoNoArvao2PBPue58zJ45azZ3Dfa0c40dnHsztruWppEVkp8cGe0jBCalpOMufP9bbWN1U0caypi0duXYOI+fAUDkxL3ZgUROQRYAMwV0QqReQWRtn0IoBNM4Lyb5fN54ZVU3l+Vx0rpmdx59ULJ3I6wwi5H3xoCbPyUtle2cpXLpnLGbNyxn+RERKmpW5MCqp64wgP3zfKsX5tmhEsT4zwvQ8u4VtXLyI+1nyuNiJPTmoCT3zuLIZUSYiNnNLCk4FJ6obhEpPQjUhmdmILT+anYhiGYRhRwiR1wzAMw4gSJqkbhmEYRpSQSNiqU0TagX1ux2GzXOCE20E4YK6qTvp6pyLSABw95eFw/plP5timq+qk34nEXLO2cu2ajZSJcvtUtcztIOwkIuXR9m8C77/L7RjCwUi/cOH8MzexGeaatY+bsZnud8MwDMOIEiapG4ZhGEaUiJSkfrfbATggGv9NEL3/LjuE8/+Nic0YSTj/35vYRhARE+UMwzAMwxhfpLTUDcMwDMMYh0nqhmEYhhElwjapi8h1IrJLRIZEpOyU5/5VRA6KyD4RucStGIMlIpdasR8Uka+7HU+wROR+EakXkZ3DHssWkedF5ID1NcvNGMNBuP68RWSqiLwkInus37UvuB3TqUTEIyJbReQpt2OZbMx1Gxy3r9mwTep497a+Fnh1+IMisgDvntYLgUuBX4hIxGwTZMX6c+D9wALgRuvfFIkexPszGO7rwIuqOgd40bo/aYX5z3sA+LKqzgfWAJ8No9h8voB3y1sjhMx1OyGuXrNhm9RVdY+qjlRF7mrgUVXtVdUjwEFgdWijm5DVwEFVPayqfcCjeP9NEUdVXwWaTnn4auAh6/uHgGtCGVMYCtuft6rWqOoW6/t2vH+Iit2N6h0iUgJcDtzrdiyTkLlugxAO12zYJvUxFAPHh92vJEx+oH6K9PjHU6CqNeD95QPyXY7HbRHx8xaRUmA58KbLoQz3E+CrwJDLcUxG5roNzk9w+Zp1NamLyAsisnOE21ifCGWExyJpXV6kx28EJux/3iKSCvwR+KKqtrkdD4CIXAHUq+pmt2OZpMx1G3g8YXHNulr7XVUvCuJllcDUYfdLgGp7IgqJSI9/PHUiUqiqNSJSCNS7HZDLwvrnLSJxeP8w/lZV/+R2PMOcBVwlIpcBiUC6iPxGVT/qclyThbluAxcW12wkdr+vB24QkQQRmQHMAd5yOaZAbALmiMgMEYnHO+lvvcsx2Wk9cJP1/U3AEy7GEg7C9uctIgLcB+xR1R+5Hc9wqvqvqlqiqqV4/8/+ZhJ6SJnrNkDhcs2GbVIXkQ+ISCVwBvAXEXkWQFV3AY8Bu4FngM+q6qB7kQZGVQeAzwHP4p3g8Zj1b4o4IvIIsAGYKyKVInIL8D1grYgcANZa9yetMP95nwV8DLhARLZZt8vcDspwn7luI5cpE2sYhmEYUSJsW+qGYRiGYQTGJHXDMAzDiBImqRuGYRhGlDBJ3TAMwzCihEnqhmEYhhElTFIPEREpFZFuEdkW4Ouut3ZJMrtUGYZhGGMyST20DqnqskBeoKq/Az7lTDhGpBKRnGFrdGtFpMr6vkNEfuHA+10z2k5YIvKgiBwRkU/b+H4/sP5d/2LXOQ13mWs2NFwtExstROTbwAlV/al1/ztAnar+bIzXlOItnvMa3u0D3wYeAO7EuwnKP6hqJFXKM0JIVRuBZQAicgfQoar/7eBbXgM8hbfo00i+oqp/sOvNVPUrItJp1/kM95lrNjRMS90e92GVRhWRGLwlAn/rx+tmAz8FlgDzgI8AZwP/AvybI5EaUU1EzvMN1YjIHSLykIg8JyIVInKtiHxfRHaIyDNW/WxEZKWIvCIim0XkWatm//BznglcBfzAalnNGieG66yNmd4WkVetxzxWS2aTiGwXkduGHf9VK6a3RWRSVyCcjMw1ay/TUreBqlaISKOILAcKgK3Wp9LxHFHVHQAisgt4UVVVRHYApc5FbEwis4DzgQV4S/p+UFW/KiKPA5eLyF+A/wGuVtUGEbke+A7wSd8JVPUNEVkPPOVny+abwCWqWiUimdZjtwCtqrpKRBKA10XkObwfZq8BTlfVLhHJtuMfbUQ0c81OgEnq9rkX+AQwBbjfz9f0Dvt+aNj9IczPxrDHX1W13/qg6ME75APg++A4F1gEPC8iWMfUTPA9XwceFJHHAN8OWhcDS0TkQ9b9DLybMV0EPKCqXQCq2jTB9zYin7lmJ8AkDvs8DnwLiMPbjW4Y4aAXQFWHRKRf39nswffBUYBdqnqGXW+oqp8WkdOBy4FtIrLMep9/UtVnhx8rIpcSZvt0G64z1+wEmDF1m6hqH/AS3t2MImbXOGPS2wfkicgZ4N2nWkQWjnBcO5DmzwlFZJaqvqmq3wRO4N2X+1ngH4eNiZ4mIinAc8AnRSTZejysujKNsGSu2TGYlrpNrAlya4Dr/DleVSvwdiH57n9itOcMwymq2md1L/5MRDLw/k34CXDqNpuPAveIyOeBD6nqoTFO+wMRmYO3pfMi3pUd2/F2nW4Rb59pA3CNqj5jtYrKRaQPeBozSdQYg7lmx2a2XrWBeNdCPgU8rqpfHuWYqcAbQGMga9WtSSC3A5tV9WM2hGsYthKRB/F/QlIg570D55c9GZNQNF+zpvvdBqq6W1VnjpbQrWOOq+rUYIrPqOoCk9CNMNYKfFtsLuQBfBRwfd2vEZWi9po1LXXDMAzDiBKmpW4YhmEYUcIkdcMwDMOIEiapG4ZhGEaUMEndMAzDMKLE/wcMK+i2GmZlbwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "timepts = np.linspace(0, Tf, 12)\n", + "poly = fs.PolyFamily(8)\n", + "traj_cost = opt.quadratic_cost(\n", + " vehicle_flat, np.diag([0, 0.1, 0]), np.diag([0.1, 10]), x0=xf, u0=uf)\n", + "constraints = [\n", + " opt.input_range_constraint(vehicle_flat, [8, -0.1], [12, 0.1]) ]\n", + "\n", + "traj3 = fs.point_to_point(\n", + " vehicle_flat, timepts, x0, u0, xf, uf, cost=traj_cost, basis=poly\n", + ")\n", + "plot_vehicle_lanechange(traj3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Vehicle transfer functions for forward and reverse driving (Example 10.11)\n", "\n", - "KJA comment, 20 Jul 2019: I have been oscillating a bit about this example. Of course it does not make sense to drive in reverse in 30 m/s but it seems a bit silly to change parameters just in this case (if we do we have to motivate it). On the other hand what we are doing is essentially based on transfer functions and a RHP zero. My current view which has changed a few times is to keep the standard parameters. In any case we should eliminate the extra time constant. A small detail, I could not see the time response in the file you sent, do not resend it!, I will look at the final version.\n", + "The vehicle steering model has different properties depending on whether we are driving forward or in reverse. The figures below show step responses from steering angle to lateral translation for a the linearized model when driving forward (dashed) and reverse (solid). In this simulation we have added an extra pole with the time constant $T=0.1$ to approximately account for the dynamics in the steering system.\n", "\n", - "RMM comment, 23 Jul 2019: I think it is OK to have the speed be different and just talk about this in the text. I have removed the extra time constant in the current version." + "With rear-wheel steering the center of mass first moves in the wrong direction and the overall response with rear-wheel steering is significantly delayed compared with that for front-wheel steering. (b) Frequency response for driving forward (dashed) and reverse (solid). Notice that the gain curves are identical, but the phase curve for driving in reverse has non-minimum phase." ] }, { @@ -927,26 +873,6 @@ "For a lane transfer system we would like to have a nice response without overshoot, and we therefore consider the use of feedforward compensation to provide a reference trajectory for the closed loop system. We choose the desired response as $F_\\text{m}(s) = a^22/(s + a)^2$, where the response speed or aggressiveness of the steering is governed by the parameter $a$." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "RMM note, 27 Jun 2019:\n", - "* $a$ was used in the original description of the dynamics as the reference offset. Perhaps choose a different symbol here?\n", - "* In current version of Ch 12, the $y$ axis is labeled in absolute units, but it should actually be in normalized units, I think.\n", - "* The steering angle input for this example is quite high. Compare to Example 8.8, above. Also, we should probably make the size of the \"lane change\" from this example match whatever we use in Example 8.8\n", - "\n", - "KJA comments, 1 Jul 2019: Chosen parameters look good to me\n", - "\n", - "RMM response, 17 Jul 2019\n", - "* I changed the time constant for the feedforward model to give something that is more reasonable in terms of turning angle at the speed of $v_0 = 30$ m/s. Note that this takes about 30 body lengths to change lanes (= 9 seconds at 105 kph).\n", - "* The time to change lanes is about 2X what it is using the differentially flat trajectory above. This is mainly because the feedback controller applies a large pulse at the beginning of the trajectory (based on the input error), whereas the differentially flat trajectory spreads the turn over a longer interval. Since are living the steering angle, we have to limit the size of the pulse => slow down the time constant for the reference model.\n", - "\n", - "KJA response, 20 Jul 2019: I think the time for lane change is too long, which may depend on the small steering angles used. The largest steering angle is about 0.03 rad, but we have admitted larger values in previous examples. I suggest that we change the design so that the largest sterring angel is closer to 0.05, see the remark from Bjorn O a lane change could take about 5 s at 30m/s. \n", - "\n", - "RMM response, 23 Jul 2019: I reset the time constant to 0.2, which gives something closer to what we had for trajectory generation. It is still slower, but this is to be expected since it is a linear controller. We now finish the trajectory in 20 body lengths, which is about 6 seconds." - ] - }, { "cell_type": "code", "execution_count": 13, @@ -1012,15 +938,6 @@ "Consider a controller based on state feedback combined with an observer where we want a faster closed loop system and choose $\\omega_\\text{c} = 10$, $\\zeta_\\text{c} = 0.707$, $\\omega_\\text{o} = 20$, and $\\zeta_\\text{o} = 0.707$." ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "KJA comment, 20 Jul 2019: This is a really troublesome case. If we keep it as a vehicle steering problem we must have an order of magnitude lower valuer for $\\omega_c$ and $\\omega_o$ and then the zero will not be slow. My recommendation is to keep it as a general system with the transfer function. $P(s)=(s+1)/s^2$. The text then has to be reworded.\n", - "\n", - "RMM response, 23 Jul 2019: I think the way we have it is OK. Our current value for the controller and observer is $\\omega_\\text{c} = 0.7$ and $\\omega_\\text{o} = 1$. Here we way we want something faster and so we got to $\\omega_\\text{c} = 7$ (10X) and $\\omega_\\text{o} = 10$ (10X)." - ] - }, { "cell_type": "code", "execution_count": 14, @@ -1146,13 +1063,6 @@ "ct.gangof4(P, C1, np.logspace(-1, 3, 100))\n", "ct.gangof4(P, C2, np.logspace(-1, 3, 100))" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -1171,7 +1081,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.2" + "version": "3.9.1" } }, "nbformat": 4, pFad - Phonifier reborn

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