本文整理匯總了Python中cvxpy.vstack方法的典型用法代碼示例。如果您正苦於以下問題：Python cvxpy.vstack方法的具體用法？Python cvxpy.vstack怎麽用？Python cvxpy.vstack使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類cvxpy的用法示例。
在下文中一共展示了cvxpy.vstack方法的5個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: synth_h2_state_feedback_LMI
# 需要導入模塊: import cvxpy [as 別名]
# 或者: from cvxpy import vstack [as 別名]
def synth_h2_state_feedback_LMI(A, Binput, Bdist, C1, D12):
#Dullerud p 217 (?)
n = A.shape[0] #num states
m = Binput.shape[1] #num control inputs
q = C1.shape[0] #num outputs to "be kept small"
X = cvxpy.Variable(n,n)
Y = cvxpy.Variable(m,n)
Z = cvxpy.Variable(q,q)
tmp1 = cvxpy.hstack(X, (C1*X+D12*Y).T)
tmp2 = cvxpy.hstack((C1*X+D12*Y), Z)
tmp = cvxpy.vstack(tmp1, tmp2)
constraints = [A*X + Binput*Y + X*A.T + Y.T*Binput.T + Bdist*Bdist.T == -cvxpy.Semidef(n),
tmp == cvxpy.Semidef(n+q),
]
obj = cvxpy.Minimize(cvxpy.trace(Z))
prob = cvxpy.Problem(obj, constraints)
prob.solve(solver='CVXOPT', kktsolver='robust')
K = -Y.value*np.linalg.inv(X.value)
return K 示例2: get_constraints
# 需要導入模塊: import cvxpy [as 別名]
# 或者: from cvxpy import vstack [as 別名]
def get_constraints(self, X_v, U_v, X_last_p, U_last_p):
"""
Get model specific constraints.
:param X_v: cvx variable for current states
:param U_v: cvx variable for current inputs
:param X_last_p: cvx parameter for last states
:param U_last_p: cvx parameter for last inputs
:return: A list of cvx constraints
"""
# Boundary conditions:
constraints = [
X_v[:, 0] == self.x_init,
X_v[:, -1] == self.x_final,
U_v[:, 0] == 0,
U_v[:, -1] == 0
]
# Input conditions:
constraints += [
0 <= U_v[0, :],
U_v[0, :] <= self.v_max,
cvx.abs(U_v[1, :]) <= self.w_max,
]
# State conditions:
constraints += [
X_v[0:2, :] <= self.upper_bound - self.robot_radius,
X_v[0:2, :] >= self.lower_bound + self.robot_radius,
]
# linearized obstacles
for j, obst in enumerate(self.obstacles):
p = obst[0]
r = obst[1] + self.robot_radius
lhs = [(X_last_p[0:2, k] - p) / (cvx.norm((X_last_p[0:2, k] - p)) + 1e-6) * (X_v[0:2, k] - p)
for k in range(K)]
constraints += [r - cvx.vstack(lhs) <= self.s_prime[j]]
return constraints 示例3: cost
# 需要導入模塊: import cvxpy [as 別名]
# 或者: from cvxpy import vstack [as 別名]
def cost(self):
T, S = self.terminals[0].power_var.shape
if self.final_energy_price is not None:
if self.energy is None:
self.energy = cvx.Variable(self.terminals[0].power_var.shape)
cost = np.zeros((T - 1, S))
final_cost = cvx.reshape(
self.energy[-1, :] * self.final_energy_price[0, 0], (1, S))
cost = cvx.vstack([cost, final_cost])
else:
cost = np.zeros(T, S)
return cost 示例4: get_constraints
# 需要導入模塊: import cvxpy [as 別名]
# 或者: from cvxpy import vstack [as 別名]
def get_constraints(self, X_v, U_v, X_last_p, U_last_p):
"""
Get model specific constraints.
:param X_v: cvx variable for current states
:param U_v: cvx variable for current inputs
:param X_last_p: cvx parameter for last states
:param U_last_p: cvx parameter for last inputs
:return: A list of cvx constraints
"""
# Boundary conditions:
constraints = [
X_v[0, 0] == self.x_init[0],
X_v[1:4, 0] == self.x_init[1:4],
X_v[4:7, 0] == self.x_init[4:7],
X_v[7:11, 0] == self.x_init[7:11],
X_v[11:14, 0] == self.x_init[11:14],
# X_[0, -1] == self.x_final[0], # final mass is free
X_v[1:, -1] == self.x_final[1:],
# U_v[1:3, -1] == 0,
]
constraints += [
# State constraints:
X_v[0, :] >= self.m_dry, # minimum mass
cvx.norm(X_v[1: 3, :], axis=0) <= X_v[3, :] / self.tan_gamma_gs, # glideslope
cvx.norm(X_v[8:10, :], axis=0) <= np.sqrt((1 - self.cos_theta_max) / 2), # maximum angle
cvx.norm(X_v[11: 14, :], axis=0) <= self.w_B_max, # maximum angular velocity
# Control constraints:
cvx.norm(U_v[0:2, :], axis=0) <= self.tan_delta_max * U_v[2, :], # gimbal angle constraint
# self.cos_delta_max * self.gamma <= U_v[2, :],
cvx.norm(U_v, axis=0) <= self.T_max, # upper thrust constraint
# U_v[2, :] >= self.T_min # simple lower thrust constraint
# # Lossless convexification:
# self.gamma <= self.T_max,
# self.T_min <= self.gamma,
# cvx.norm(U_v, axis=0) <= self.gamma
]
# linearized lower thrust constraint
lhs = [U_last_p[:, k] / (cvx.norm(U_last_p[:, k])) * U_v[:, k] for k in range(K)]
constraints += [
self.T_min - cvx.vstack(lhs) <= self.s_prime
]
return constraints 示例5: get_constraints
# 需要導入模塊: import cvxpy [as 別名]
# 或者: from cvxpy import vstack [as 別名]
def get_constraints(self, X_v, U_v, X_last_p, U_last_p):
"""
Get model specific constraints.
:param X_v: cvx variable for current states
:param U_v: cvx variable for current inputs
:param X_last_p: cvx parameter for last states
:param U_last_p: cvx parameter for last inputs
:return: A list of cvx constraints
"""
# Boundary conditions:
constraints = [
X_v[0, 0] == self.x_init[0],
X_v[1:4, 0] == self.x_init[1:4],
X_v[4:7, 0] == self.x_init[4:7],
# X_v[7:11, 0] == self.x_init[7:11], # initial orientation is free
X_v[11:14, 0] == self.x_init[11:14],
# X_[0, -1] == self.x_final[0], # final mass is free
X_v[1:, -1] == self.x_final[1:],
U_v[1:3, -1] == 0,
]
constraints += [
# State constraints:
X_v[0, :] >= self.m_dry, # minimum mass
cvx.norm(X_v[2: 4, :], axis=0) <= X_v[1, :] / self.tan_gamma_gs, # glideslope
cvx.norm(X_v[9:11, :], axis=0) <= np.sqrt((1 - self.cos_theta_max) / 2), # maximum angle
cvx.norm(X_v[11: 14, :], axis=0) <= self.w_B_max, # maximum angular velocity
# Control constraints:
cvx.norm(U_v[1:3, :], axis=0) <= self.tan_delta_max * U_v[0, :], # gimbal angle constraint
cvx.norm(U_v, axis=0) <= self.T_max, # upper thrust constraint
# U_v[0, :] >= self.T_min # simple lower thrust constraint
]
# linearized lower thrust constraint
rhs = [U_last_p[:, k] / cvx.norm(U_last_p[:, k]) * U_v[:, k] for k in range(X_v.shape[1])]
constraints += [
self.T_min <= cvx.vstack(rhs)
]
return constraints