def define_ha(settings, usafe_r=None):
'''make the hybrid automaton and return it'''
ha = LinearHybridAutomaton()
ha.variables = ["x1", "x2", "x3", "x4"]
#
loc1 = ha.new_mode('loc1')
a_matrix = np.array([[1, 0, 0.1, 0], [0, 1, 0, 0.1],
[0, 0, 0.8870, 0.0089], [0, 0, 0.0089, 0.8870]],
dtype=float)
# exp 1
b_matrix = np.array([[1, 0], [0, 0], [1, 0], [0, 1]], dtype=float)
print(a_matrix, b_matrix)
R_mult_factor = 0.1
Q_matrix = np.eye(len(a_matrix[0]), dtype=float)
u_dim = len(b_matrix[0])
R_matrix = R_mult_factor * np.eye(u_dim)
print(a_matrix, b_matrix, Q_matrix, R_matrix)
k_matrix = get_input(a_matrix, b_matrix, Q_matrix, R_matrix)
print(k_matrix)
a_bk_matrix = a_matrix - np.matmul(b_matrix, k_matrix)
loc1.a_matrix = a_bk_matrix
loc1.c_vector = np.array([0.0, 0.0, 0.0, 0.0], dtype=float)
# print(a_bk_matrix)
error = ha.new_mode('_error')
error.is_error = True
usafe_set_constraint_list = []
if usafe_r is None:
# exp 1
# significant diff (10 sec) across equivalent/non-equ runs for p_intersect without reverse
# usafe_set_constraint_list.append(LinearConstraint([1.0, 0.0, 0.0, 0.0], -4.8))
# exp 2
# significant diff (13-15 sec) across equivalent/non-equ runs for p_intersect without reverse
# usafe_set_constraint_list.append(LinearConstraint([0.0, 0.0, 1.0, 0.0], -5.0))
# exp 3
usafe_set_constraint_list.append(
LinearConstraint([1.0, 0.0, 0.0, 0.0], -5.2))
else:
usafe_star = init_hr_to_star(settings, usafe_r, ha.modes['_error'])
for constraint in usafe_star.constraint_list:
usafe_set_constraint_list.append(constraint)
trans = ha.new_transition(loc1, error)
for constraint in usafe_set_constraint_list:
trans.condition_list.append(constraint)
return ha, usafe_set_constraint_list
def define_ha(settings, usafe_r=None):
'''make the hybrid automaton and return it'''
ha = LinearHybridAutomaton()
ha.variables = ["x1", "x2"]
#
loc1 = ha.new_mode('loc1')
# exp 1 and 2
a_matrix = np.array([[0.983498664120250, 0.101548195541291],
[-0.013528375561473, 0.935610369333783]],
dtype=float)
# exp 1
b_matrix = np.array([[0.0], [0.0]], dtype=float)
# # exp2
# b_matrix = np.array([[1], [1]], dtype=float)
print(a_matrix, b_matrix)
R_mult_factor = 0.2
Q_matrix = np.eye(len(a_matrix[0]), dtype=float)
u_dim = len(b_matrix[0])
R_matrix = R_mult_factor * np.eye(u_dim)
print(a_matrix, b_matrix, Q_matrix, R_matrix)
k_matrix = get_input(a_matrix, b_matrix, Q_matrix, R_matrix)
print(k_matrix)
# a_bk_matrix = a_matrix_ext - np.matmul(b_matrix_ext, k_matrix)
a_bk_matrix = a_matrix - np.matmul(b_matrix, k_matrix)
loc1.a_matrix = a_bk_matrix
loc1.c_vector = np.array([0.0, 0.0], dtype=float)
error = ha.new_mode('_error')
error.is_error = True
usafe_set_constraint_list = []
if usafe_r is None:
# exp 1
usafe_set_constraint_list.append(LinearConstraint([-1.0, 0.0], -2.0))
# usafe_set_constraint_list.append(LinearConstraint([0.0, 1.0], -0.85))
else:
usafe_star = init_hr_to_star(settings, usafe_r, ha.modes['_error'])
for constraint in usafe_star.constraint_list:
usafe_set_constraint_list.append(constraint)
trans = ha.new_transition(loc1, error)
for constraint in usafe_set_constraint_list:
trans.condition_list.append(constraint)
return ha, usafe_set_constraint_list
def define_ha(settings, usafe_r=None):
'''make the hybrid automaton and return it'''
ha = LinearHybridAutomaton()
ha.variables = ["x1", "x2", "x3", "x4"]
#
loc1 = ha.new_mode('loc1')
a_matrix = np.array(
[[1, 0, 0, 0], [0, 2, 0.5, 0], [0, 0, 1, 0], [0, 0, 0, 0.5]],
dtype=float)
# exp 1
b_matrix = np.array(
[[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, 3, 0], [0, 2, 0, 1]], dtype=float)
print(a_matrix, b_matrix)
R_mult_factor = 0.1
Q_matrix = np.eye(len(a_matrix[0]), dtype=float)
u_dim = len(b_matrix[0])
R_matrix = R_mult_factor * np.eye(u_dim)
print(a_matrix, b_matrix, Q_matrix, R_matrix)
k_matrix = get_input(a_matrix, b_matrix, Q_matrix, R_matrix)
print(k_matrix)
a_bk_matrix = a_matrix - np.matmul(b_matrix, k_matrix)
loc1.a_matrix = a_bk_matrix
loc1.c_vector = np.array([0.0, 0.0, 0.0, 0.0], dtype=float)
# print(a_bk_matrix)
error = ha.new_mode('_error')
error.is_error = True
usafe_set_constraint_list = []
if usafe_r is None:
# exp 1
# usafe_set_constraint_list.append(LinearConstraint([0.0, 0.0, 0.0, 1.0], -2.9))
# exp 2
usafe_set_constraint_list.append(
LinearConstraint([0.0, 0.0, 0.0, 1.0], -3.42))
else:
usafe_star = init_hr_to_star(settings, usafe_r, ha.modes['_error'])
for constraint in usafe_star.constraint_list:
usafe_set_constraint_list.append(constraint)
trans = ha.new_transition(loc1, error)
for constraint in usafe_set_constraint_list:
trans.condition_list.append(constraint)
return ha, usafe_set_constraint_list
def define_ha(settings, usafe_r=None):
'''make the hybrid automaton and return it'''
ha = LinearHybridAutomaton()
ha.variables = ["x1", "x2", "x3", "x4", "x5", "x6"]
#
loc1 = ha.new_mode('loc1')
a_matrix = np.array([[0, 1, 0, 0, 0, 0], [0, 0, -0.0106, 0.0106, -0.0106, 0.0106], [0, 0, -10, 0, 0, 0],
[0, 0, 0, -10, 0, 0], [0, 0, 0, 0, -10, 0], [0, 0, 0, 0, 0, -10]], dtype=float)
b_matrix = np.array([[0], [0], [1], [-1], [1], [-1]], dtype=float)
print(a_matrix, b_matrix)
R_mult_factor = 0.01
Q_matrix = np.eye(len(a_matrix[0]), dtype=float)
u_dim = len(b_matrix[0])
R_matrix = R_mult_factor * np.eye(u_dim)
print(a_matrix, b_matrix, Q_matrix, R_matrix)
k_matrix = get_input(a_matrix, b_matrix, Q_matrix, R_matrix)
print(k_matrix)
a_bk_matrix = a_matrix - np.matmul(b_matrix, k_matrix)
loc1.a_matrix = a_bk_matrix
loc1.c_vector = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0], dtype=float)
# print(a_bk_matrix)
error = ha.new_mode('_error')
error.is_error = True
usafe_set_constraint_list = []
if usafe_r is None:
# exp 1
# usafe_set_constraint_list.append(LinearConstraint([-1.0, 0.0, 0.0, 0.0, 0.0, 0.0], -0.4))
# exp 2
usafe_set_constraint_list.append(LinearConstraint([0.0, 0.0, -1.0, 0.0, 0.0, 0.0], -0.41))
# To find the error (reverse - number of counterexamples reduced in equ run)
# usafe_set_constraint_list.append(LinearConstraint([0.0, 0.0, -1.0, 0.0, 0.0, 0.0], -0.47))
# exp 3 To find the error (reverse - number of counterexamples increased in equ run)
# Gets fixed with following values
# bigM = 10000.0
# lb = -1000.0
# ub = 1000.0
# or epsilon2 = 0.00001 (Earlier it was 0.0005)
# usafe_set_constraint_list.append(LinearConstraint([-1.0, 0.0, 0.0, 0.0, 0.0, 0.0], -0.4))
else:
usafe_star = init_hr_to_star(settings, usafe_r, ha.modes['_error'])
for constraint in usafe_star.constraint_list:
usafe_set_constraint_list.append(constraint)
trans = ha.new_transition(loc1, error)
for constraint in usafe_set_constraint_list:
trans.condition_list.append(constraint)
return ha, usafe_set_constraint_list
def define_ha(settings, usafe_r=None):
'''make the hybrid automaton and return it'''
ha = LinearHybridAutomaton()
ha.variables = ["x1", "x2"]
#
loc1 = ha.new_mode('loc1')
# a_matrix = np.array([[0.0, 2.0], [1.0, 0.0]], dtype=float)
# exp 1 and 2
a_matrix = np.array([[0.0, 2.0], [-1.5, 0.0]], dtype=float)
# exp 1
b_matrix = np.array([[1], [-1]], dtype=float)
# # exp2
# b_matrix = np.array([[1], [1]], dtype=float)
print(a_matrix, b_matrix)
R_mult_factor = 0.2
Q_matrix = np.eye(len(a_matrix[0]), dtype=float)
u_dim = len(b_matrix[0])
R_matrix = R_mult_factor * np.eye(u_dim)
print(a_matrix, b_matrix, Q_matrix, R_matrix)
k_matrix = get_input(a_matrix, b_matrix, Q_matrix, R_matrix)
print(k_matrix)
# a_bk_matrix = a_matrix_ext - np.matmul(b_matrix_ext, k_matrix)
a_bk_matrix = a_matrix - np.matmul(b_matrix, k_matrix)
# print(a_bk_matrix)
loc1.a_matrix = a_bk_matrix
loc1.c_vector = np.array([0.0, 0.0], dtype=float)
# print(a_bk_matrix)
# loc1.a_matrix = np.array([[0.0, 2.0], [1.0, 0.0]], dtype=float)
# loc1.c_vector = np.array([0.0, -9.81], dtype=float)
error = ha.new_mode('_error')
error.is_error = True
usafe_set_constraint_list = []
if usafe_r is None:
# exp 1
usafe_set_constraint_list.append(LinearConstraint([-1.0, 0.0], -2.0))
# exp 2 - Significant diff across equivalent and non-equivalent runs for p_intersect reverse
# usafe_set_constraint_list.append(LinearConstraint([0.0, 1.0], -0.85))
else:
usafe_star = init_hr_to_star(settings, usafe_r, ha.modes['_error'])
for constraint in usafe_star.constraint_list:
usafe_set_constraint_list.append(constraint)
trans = ha.new_transition(loc1, error)
for constraint in usafe_set_constraint_list:
trans.condition_list.append(constraint)
return ha, usafe_set_constraint_list
