diff --git a/control/passivity.py b/control/passivity.py
index 3777b3d92..0f4104186 100644
--- a/control/passivity.py
+++ b/control/passivity.py
@@ -14,7 +14,6 @@
except ImportError:
cvx = None
-eps = np.nextafter(0, 1)
__all__ = ["get_output_fb_index", "get_input_ff_index", "ispassive",
"solve_passivity_LMI"]
@@ -28,8 +27,8 @@ def solve_passivity_LMI(sys, rho=None, nu=None):
passive. Inputs of None for `rho` or `nu` indicate that the function should
solve for that index (they are mutually exclusive, they can't both be
None, otherwise you're trying to solve a nonconvex bilinear matrix
- inequality.) The last element of `solution` is either the output or input
- passivity index, for `rho` = None and `nu` = None respectively.
+ inequality.) The last element of the output `solution` is either the output or input
+ passivity index, for `rho` = None and `nu` = None respectively.
The sources for the algorithm are:
@@ -74,11 +73,8 @@ def solve_passivity_LMI(sys, rho=None, nu=None):
D = sys.D
# account for strictly proper systems
- [n, m] = D.shape
- D = D + eps * np.eye(n, m)
-
+ [_, m] = D.shape
[n, _] = A.shape
- A = A - eps*np.eye(n)
def make_LMI_matrix(P, rho, nu, one):
q = sys.noutputs
@@ -113,7 +109,7 @@ def make_P_basis_matrices(n, rho, nu):
P[i, j] = 1
P[j, i] = 1
matrix_list.append(make_LMI_matrix(P, 0, 0, 0).flatten())
- zeros = eps*np.eye(n)
+ zeros = 0.0*np.eye(n)
if rho is None:
matrix_list.append(make_LMI_matrix(zeros, 1, 0, 0).flatten())
elif nu is None:
@@ -149,9 +145,9 @@ def P_pos_def_constraint(n):
if rho is not None and nu is not None:
sys_constants = -make_LMI_matrix(np.zeros_like(A), rho, nu, 1.0)
elif rho is not None:
- sys_constants = -make_LMI_matrix(np.zeros_like(A), rho, eps, 1.0)
+ sys_constants = -make_LMI_matrix(np.zeros_like(A), rho, 0.0, 1.0)
elif nu is not None:
- sys_constants = -make_LMI_matrix(np.zeros_like(A), eps, nu, 1.0)
+ sys_constants = -make_LMI_matrix(np.zeros_like(A), 0.0, nu, 1.0)
sys_coefficents = np.vstack(sys_matrix_list).T
@@ -174,8 +170,15 @@ def P_pos_def_constraint(n):
# crunch feasibility solution
cvx.solvers.options['show_progress'] = False
- sol = cvx.solvers.sdp(c, Gs=Gs, hs=hs)
- return sol["x"]
+ try:
+ sol = cvx.solvers.sdp(c, Gs=Gs, hs=hs)
+ return sol["x"]
+
+ except ZeroDivisionError as e:
+ raise ValueError("The system is probably ill conditioned. "
+ "Consider perturbing the system matrices by a small amount."
+ ) from e
+
def get_output_fb_index(sys):
@@ -194,7 +197,7 @@ def get_output_fb_index(sys):
float
The OFP index
"""
- sol = solve_passivity_LMI(sys, nu=eps)
+ sol = solve_passivity_LMI(sys, nu=0.0)
if sol is None:
raise RuntimeError("LMI passivity problem is infeasible")
else:
@@ -218,7 +221,7 @@ def get_input_ff_index(sys):
float
The IFP index
"""
- sol = solve_passivity_LMI(sys, rho=eps)
+ sol = solve_passivity_LMI(sys, rho=0.0)
if sol is None:
raise RuntimeError("LMI passivity problem is infeasible")
else:
diff --git a/control/tests/passivity_test.py b/control/tests/passivity_test.py
index 6cee1bdb6..4d7c8e6eb 100644
--- a/control/tests/passivity_test.py
+++ b/control/tests/passivity_test.py
@@ -99,16 +99,14 @@ def test_system_dimension():
@pytest.mark.parametrize(
- "systemmatrices, expected",
- [((A, B, C, D*0.0), True),
+ "system_matrices, expected",
+ [((A, B, C, D*0), True),
((A_d, B, C, D), True),
- pytest.param((A*1e12, B, C, D*0), True,
- marks=pytest.mark.xfail(reason="gh-761")),
((A, B*0, C*0, D), True),
((A*0, B, C, D), True),
((A*0, B*0, C*0, D*0), True)])
-def test_ispassive_edge_cases(systemmatrices, expected):
- sys = ss(*systemmatrices)
+def test_ispassive_edge_cases(system_matrices, expected):
+ sys = ss(*system_matrices)
assert passivity.ispassive(sys) == expected
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