diff --git a/doc/examples.rst b/doc/examples.rst
index 1d8ac3f42..41e2b42d6 100644
--- a/doc/examples.rst
+++ b/doc/examples.rst
@@ -33,6 +33,8 @@ other sources.
steering-gainsched
steering-optimal
kincar-flatsys
+ mrac_siso_mit
+ mrac_siso_lyapunov
Jupyter notebooks
=================
diff --git a/doc/mrac_siso_lyapunov.py b/doc/mrac_siso_lyapunov.py
new file mode 120000
index 000000000..aaccf5585
--- /dev/null
+++ b/doc/mrac_siso_lyapunov.py
@@ -0,0 +1 @@
+../examples/mrac_siso_lyapunov.py
\ No newline at end of file
diff --git a/doc/mrac_siso_lyapunov.rst b/doc/mrac_siso_lyapunov.rst
new file mode 100644
index 000000000..525968882
--- /dev/null
+++ b/doc/mrac_siso_lyapunov.rst
@@ -0,0 +1,15 @@
+Model-Reference Adaptive Control (MRAC) SISO, direct Lyapunov rule
+------------------------------------------------------------------
+
+Code
+....
+.. literalinclude:: mrac_siso_lyapunov.py
+ :language: python
+ :linenos:
+
+
+Notes
+.....
+
+1. The environment variable `PYCONTROL_TEST_EXAMPLES` is used for
+testing to turn off plotting of the outputs.
\ No newline at end of file
diff --git a/doc/mrac_siso_mit.py b/doc/mrac_siso_mit.py
new file mode 120000
index 000000000..b6a226f7c
--- /dev/null
+++ b/doc/mrac_siso_mit.py
@@ -0,0 +1 @@
+../examples/mrac_siso_mit.py
\ No newline at end of file
diff --git a/doc/mrac_siso_mit.rst b/doc/mrac_siso_mit.rst
new file mode 100644
index 000000000..8be834d6d
--- /dev/null
+++ b/doc/mrac_siso_mit.rst
@@ -0,0 +1,15 @@
+Model-Reference Adaptive Control (MRAC) SISO, direct MIT rule
+-------------------------------------------------------------
+
+Code
+....
+.. literalinclude:: mrac_siso_mit.py
+ :language: python
+ :linenos:
+
+
+Notes
+.....
+
+1. The environment variable `PYCONTROL_TEST_EXAMPLES` is used for
+testing to turn off plotting of the outputs.0
\ No newline at end of file
diff --git a/examples/mrac_siso_lyapunov.py b/examples/mrac_siso_lyapunov.py
new file mode 100644
index 000000000..00dbf63aa
--- /dev/null
+++ b/examples/mrac_siso_lyapunov.py
@@ -0,0 +1,174 @@
+# mrac_siso_lyapunov.py
+# Johannes Kaisinger, 3 July 2023
+#
+# Demonstrate a MRAC example for a SISO plant using Lyapunov rule.
+# Based on [1] Ex 5.7, Fig 5.12 & 5.13.
+# Notation as in [2].
+#
+# [1] K. J. Aström & B. Wittenmark "Adaptive Control" Second Edition, 2008.
+#
+# [2] Nhan T. Nguyen "Model-Reference Adaptive Control", 2018.
+
+import numpy as np
+import scipy.signal as signal
+import matplotlib.pyplot as plt
+
+import control as ct
+
+# Plant model as linear state-space system
+A = -1
+B = 0.5
+C = 1
+D = 0
+
+io_plant = ct.ss(A, B, C, D,
+ inputs=('u'), outputs=('x'), states=('x'), name='plant')
+
+# Reference model as linear state-space system
+Am = -2
+Bm = 2
+Cm = 1
+Dm = 0
+
+io_ref_model = ct.ss(Am, Bm, Cm, Dm,
+ inputs=('r'), outputs=('xm'), states=('xm'), name='ref_model')
+
+# Adaptive control law, u = kx*x + kr*r
+kr_star = (Bm)/B
+print(f"Optimal value for {kr_star = }")
+kx_star = (Am-A)/B
+print(f"Optimal value for {kx_star = }")
+
+def adaptive_controller_state(_t, xc, uc, params):
+ """Internal state of adaptive controller, f(t,x,u;p)"""
+
+ # Parameters
+ gam = params["gam"]
+ signB = params["signB"]
+
+ # Controller inputs
+ r = uc[0]
+ xm = uc[1]
+ x = uc[2]
+
+ # Controller states
+ # x1 = xc[0] # kr
+ # x2 = xc[1] # kx
+
+ # Algebraic relationships
+ e = xm - x
+
+ # Controller dynamics
+ d_x1 = gam*r*e*signB
+ d_x2 = gam*x*e*signB
+
+ return [d_x1, d_x2]
+
+def adaptive_controller_output(_t, xc, uc, params):
+ """Algebraic output from adaptive controller, g(t,x,u;p)"""
+
+ # Controller inputs
+ r = uc[0]
+ #xm = uc[1]
+ x = uc[2]
+
+ # Controller state
+ kr = xc[0]
+ kx = xc[1]
+
+ # Control law
+ u = kx*x + kr*r
+
+ return [u]
+
+params={"gam":1, "Am":Am, "Bm":Bm, "signB":np.sign(B)}
+
+io_controller = ct.nlsys(
+ adaptive_controller_state,
+ adaptive_controller_output,
+ inputs=('r', 'xm', 'x'),
+ outputs=('u'),
+ states=2,
+ params=params,
+ name='control',
+ dt=0
+)
+
+# Overall closed loop system
+io_closed = ct.interconnect(
+ [io_plant, io_ref_model, io_controller],
+ connections=[
+ ['plant.u', 'control.u'],
+ ['control.xm', 'ref_model.xm'],
+ ['control.x', 'plant.x']
+ ],
+ inplist=['control.r', 'ref_model.r'],
+ outlist=['plant.x', 'control.u'],
+ dt=0
+)
+
+# Set simulation duration and time steps
+Tend = 100
+dt = 0.1
+
+# Define simulation time
+t_vec = np.arange(0, Tend, dt)
+
+# Define control reference input
+r_vec = np.zeros((2, len(t_vec)))
+rect = signal.square(2 * np.pi * 0.05 * t_vec)
+r_vec[0, :] = rect
+r_vec[1, :] = r_vec[0, :]
+
+plt.figure(figsize=(16,8))
+plt.plot(t_vec, r_vec[0,:])
+plt.title(r'reference input $r$')
+plt.show()
+
+# Set initial conditions, io_closed
+X0 = np.zeros((4, 1))
+X0[0] = 0 # state of plant, (x)
+X0[1] = 0 # state of ref_model, (xm)
+X0[2] = 0 # state of controller, (kr)
+X0[3] = 0 # state of controller, (kx)
+
+# Simulate the system with different gammas
+tout1, yout1, xout1 = ct.input_output_response(io_closed, t_vec, r_vec, X0,
+ return_x=True, params={"gam":0.2})
+tout2, yout2, xout2 = ct.input_output_response(io_closed, t_vec, r_vec, X0,
+ return_x=True, params={"gam":1.0})
+tout3, yout3, xout3 = ct.input_output_response(io_closed, t_vec, r_vec, X0,
+ return_x=True, params={"gam":5.0})
+
+plt.figure(figsize=(16,8))
+plt.subplot(2,1,1)
+plt.plot(tout1, yout1[0,:], label=r'$x_{\gamma = 0.2}$')
+plt.plot(tout2, yout2[0,:], label=r'$x_{\gamma = 1.0}$')
+plt.plot(tout2, yout3[0,:], label=r'$x_{\gamma = 5.0}$')
+plt.plot(tout1, xout1[1,:] ,label=r'$x_{m}$', color='black', linestyle='--')
+plt.legend(fontsize=14)
+plt.title(r'system response $x, (x_m)$')
+plt.subplot(2,1,2)
+plt.plot(tout1, yout1[1,:], label=r'$u_{\gamma = 0.2}$')
+plt.plot(tout2, yout2[1,:], label=r'$u_{\gamma = 1.0}$')
+plt.plot(tout3, yout3[1,:], label=r'$u_{\gamma = 5.0}$')
+plt.legend(loc=4, fontsize=14)
+plt.title(r'control $u$')
+plt.show()
+
+plt.figure(figsize=(16,8))
+plt.subplot(2,1,1)
+plt.plot(tout1, xout1[2,:], label=r'$k_{r, \gamma = 0.2}$')
+plt.plot(tout2, xout2[2,:], label=r'$k_{r, \gamma = 1.0}$')
+plt.plot(tout3, xout3[2,:], label=r'$k_{r, \gamma = 5.0}$')
+plt.hlines(kr_star, 0, Tend, label=r'$k_r^{\ast}$', color='black', linestyle='--')
+plt.legend(loc=4, fontsize=14)
+plt.title(r'control gain $k_r$ (feedforward)')
+plt.subplot(2,1,2)
+plt.plot(tout1, xout1[3,:], label=r'$k_{x, \gamma = 0.2}$')
+plt.plot(tout2, xout2[3,:], label=r'$k_{x, \gamma = 1.0}$')
+plt.plot(tout3, xout3[3,:], label=r'$k_{x, \gamma = 5.0}$')
+plt.hlines(kx_star, 0, Tend, label=r'$k_x^{\ast}$', color='black', linestyle='--')
+plt.legend(loc=4, fontsize=14)
+plt.title(r'control gain $k_x$ (feedback)')
+plt.show()
diff --git a/examples/mrac_siso_mit.py b/examples/mrac_siso_mit.py
new file mode 100644
index 000000000..1f87dd5f6
--- /dev/null
+++ b/examples/mrac_siso_mit.py
@@ -0,0 +1,182 @@
+# mrac_siso_mit.py
+# Johannes Kaisinger, 3 July 2023
+#
+# Demonstrate a MRAC example for a SISO plant using MIT rule.
+# Based on [1] Ex 5.2, Fig 5.5 & 5.6.
+# Notation as in [2].
+#
+# [1] K. J. Aström & B. Wittenmark "Adaptive Control" Second Edition, 2008.
+#
+# [2] Nhan T. Nguyen "Model-Reference Adaptive Control", 2018.
+
+import numpy as np
+import scipy.signal as signal
+import matplotlib.pyplot as plt
+
+import control as ct
+
+# Plant model as linear state-space system
+A = -1.
+B = 0.5
+C = 1
+D = 0
+
+io_plant = ct.ss(A, B, C, D,
+ inputs=('u'), outputs=('x'), states=('x'), name='plant')
+
+# Reference model as linear state-space system
+Am = -2
+Bm = 2
+Cm = 1
+Dm = 0
+
+io_ref_model = ct.ss(Am, Bm, Cm, Dm,
+ inputs=('r'), outputs=('xm'), states=('xm'), name='ref_model')
+
+# Adaptive control law, u = kx*x + kr*r
+kr_star = (Bm)/B
+print(f"Optimal value for {kr_star = }")
+kx_star = (Am-A)/B
+print(f"Optimal value for {kx_star = }")
+
+def adaptive_controller_state(t, xc, uc, params):
+ """Internal state of adaptive controller, f(t,x,u;p)"""
+
+ # Parameters
+ gam = params["gam"]
+ Am = params["Am"]
+ Bm = params["Bm"]
+ signB = params["signB"]
+
+ # Controller inputs
+ r = uc[0]
+ xm = uc[1]
+ x = uc[2]
+
+ # Controller states
+ x1 = xc[0] #
+ # x2 = xc[1] # kr
+ x3 = xc[2] #
+ # x4 = xc[3] # kx
+
+ # Algebraic relationships
+ e = xm - x
+
+ # Controller dynamics
+ d_x1 = Am*x1 + Am*r
+ d_x2 = - gam*x1*e*signB
+ d_x3 = Am*x3 + Am*x
+ d_x4 = - gam*x3*e*signB
+
+ return [d_x1, d_x2, d_x3, d_x4]
+
+def adaptive_controller_output(t, xc, uc, params):
+ """Algebraic output from adaptive controller, g(t,x,u;p)"""
+
+ # Controller inputs
+ r = uc[0]
+ # xm = uc[1]
+ x = uc[2]
+
+ # Controller state
+ kr = xc[1]
+ kx = xc[3]
+
+ # Control law
+ u = kx*x + kr*r
+
+ return [u]
+
+params={"gam":1, "Am":Am, "Bm":Bm, "signB":np.sign(B)}
+
+io_controller = ct.nlsys(
+ adaptive_controller_state,
+ adaptive_controller_output,
+ inputs=('r', 'xm', 'x'),
+ outputs=('u'),
+ states=4,
+ params=params,
+ name='control',
+ dt=0
+)
+
+# Overall closed loop system
+io_closed = ct.interconnect(
+ [io_plant, io_ref_model, io_controller],
+ connections=[
+ ['plant.u', 'control.u'],
+ ['control.xm', 'ref_model.xm'],
+ ['control.x', 'plant.x']
+ ],
+ inplist=['control.r', 'ref_model.r'],
+ outlist=['plant.x', 'control.u'],
+ dt=0
+)
+
+# Set simulation duration and time steps
+Tend = 100
+dt = 0.1
+
+# Define simulation time
+t_vec = np.arange(0, Tend, dt)
+
+# Define control reference input
+r_vec = np.zeros((2, len(t_vec)))
+square = signal.square(2 * np.pi * 0.05 * t_vec)
+r_vec[0, :] = square
+r_vec[1, :] = r_vec[0, :]
+
+plt.figure(figsize=(16,8))
+plt.plot(t_vec, r_vec[0,:])
+plt.title(r'reference input $r$')
+plt.show()
+
+# Set initial conditions, io_closed
+X0 = np.zeros((6, 1))
+X0[0] = 0 # state of plant, (x)
+X0[1] = 0 # state of ref_model, (xm)
+X0[2] = 0 # state of controller,
+X0[3] = 0 # state of controller, (kr)
+X0[4] = 0 # state of controller,
+X0[5] = 0 # state of controller, (kx)
+
+# Simulate the system with different gammas
+tout1, yout1, xout1 = ct.input_output_response(io_closed, t_vec, r_vec, X0,
+ return_x=True, params={"gam":0.2})
+tout2, yout2, xout2 = ct.input_output_response(io_closed, t_vec, r_vec, X0,
+ return_x=True, params={"gam":1.0})
+tout3, yout3, xout3 = ct.input_output_response(io_closed, t_vec, r_vec, X0,
+ return_x=True, params={"gam":5.0})
+
+plt.figure(figsize=(16,8))
+plt.subplot(2,1,1)
+plt.plot(tout1, yout1[0,:], label=r'$x_{\gamma = 0.2}$')
+plt.plot(tout2, yout2[0,:], label=r'$x_{\gamma = 1.0}$')
+plt.plot(tout2, yout3[0,:], label=r'$x_{\gamma = 5.0}$')
+plt.plot(tout1, xout1[1,:] ,label=r'$x_{m}$', color='black', linestyle='--')
+plt.legend(fontsize=14)
+plt.title(r'system response $x, (x_m)$')
+plt.subplot(2,1,2)
+plt.plot(tout1, yout1[1,:], label=r'$u_{\gamma = 0.2}$')
+plt.plot(tout2, yout2[1,:], label=r'$u_{\gamma = 1.0}$')
+plt.plot(tout3, yout3[1,:], label=r'$u_{\gamma = 5.0}$')
+plt.legend(loc=4, fontsize=14)
+plt.title(r'control $u$')
+plt.show()
+
+plt.figure(figsize=(16,8))
+plt.subplot(2,1,1)
+plt.plot(tout1, xout1[3,:], label=r'$k_{r, \gamma = 0.2}$')
+plt.plot(tout2, xout2[3,:], label=r'$k_{r, \gamma = 1.0}$')
+plt.plot(tout3, xout3[3,:], label=r'$k_{r, \gamma = 5.0}$')
+plt.hlines(kr_star, 0, Tend, label=r'$k_r^{\ast}$', color='black', linestyle='--')
+plt.legend(loc=4, fontsize=14)
+plt.title(r'control gain $k_r$ (feedforward)')
+plt.subplot(2,1,2)
+plt.plot(tout1, xout1[5,:], label=r'$k_{x, \gamma = 0.2}$')
+plt.plot(tout2, xout2[5,:], label=r'$k_{x, \gamma = 1.0}$')
+plt.plot(tout3, xout3[5,:], label=r'$k_{x, \gamma = 5.0}$')
+plt.hlines(kx_star, 0, Tend, label=r'$k_x^{\ast}$', color='black', linestyle='--')
+plt.legend(loc=4, fontsize=14)
+plt.title(r'control gain $k_x$ (feedback)')
+plt.show()
\ No newline at end of file
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