x = vertcat(p1, v1, p2, v2)
# controls
a = SX.sym('a')
dt = SX.sym('dt')
u = vertcat(a, dt)
f_expl = dt * vertcat(v1, a, v2, -beta * v2 - k * (p2 - p1))
model = AcadosModel()
model.f_expl_expr = f_expl
model.x = x
model.u = u
model.name = 'crane_time_opt'
# create ocp object to formulate the OCP
x0 = np.array([2.0, 0.0, 2.0, 0.0])
xf = np.array([0.0, 0.0, 0.0, 0.0])
ocp = AcadosOcp()
ocp.model = model
# N - maximum number of bangs
N = 7
Tf = N
nx = model.x.size()[0]
nu = model.u.size()[0]
def acados_nonlinear_quad_model(num_states, g=9.81):
model_name = 'nonlinear_quad'
position = SX.sym('o', 3)
velocity = SX.sym('v', 3)
phi = SX.sym('phi')
theta = SX.sym('theta')
psi = SX.sym('psi')
gravity = SX.zeros(3)
gravity[2] = g
x = vertcat(position, phi, theta, psi, velocity)
p = SX.sym('p')
q = SX.sym('q')
r = SX.sym('r')
F = SX.sym('F')
u = vertcat(p, q, r, F)
r1 = SX(3, 3)
r1[0, 0] = 1
r1[1, 1] = cos(phi)
r1[1, 2] = -sin(phi)
r1[2, 1] = sin(phi)
r1[2, 2] = cos(phi)
r2 = SX(3, 3)
r2[0, 0] = cos(theta)
r2[0, 2] = sin(theta)
r2[1, 1] = 1
r2[2, 0] = -sin(theta)
r2[2, 2] = cos(theta)
r3 = SX(3, 3)
r3[0, 0] = cos(psi)
r3[0, 1] = -sin(psi)
r3[1, 0] = sin(psi)
r3[1, 1] = cos(psi)
r3[2, 2] = 1
tau = SX(3, 3)
tau[:, 0] = (inv(r2))[:, 0]
tau[:, 1] = (inv(r2))[:, 1]
tau[:, 2] = (inv(r2 @ r1))[:, 2]
# tau[:,0] = (inv(r1))[:,0]
# tau[:,1] = (inv(r1))[:,1]
# tau[:,2] = (inv(r1 @ r2))[:,2]
# tau[0,0] = cos(theta)
# tau[0,2] = -sin(theta)
# tau[1,1] = 1
# tau[1,2] = sin(phi) * cos(theta)
# tau[2,0] = sin(theta)
# tau[2,2] = cos(phi) * cos(theta)
tc = 0.59375 # mapping control to throttle
# f_const = 100
f_const = 20
# tc = 0.75
#
# scaling_control_mat = np.array([1, 1, 1, (g/tc)])
# f_expl = vertcat(
# velocity,
# inv(tau) @ u[0:3],
# - ((r3 @ r1 @ r2)[:,2]) * (u[3] * ((g)/tc)) + gravity
# )
f_expl = vertcat(
velocity,
inv(tau) @ u[0:3], -((r3 @ r1 @ r2)[:, 2]) *
(u[3] * ((g + f_const) / tc) - f_const) + gravity)
# f_expl = vertcat(
# velocity,
# inv(tau) @ u[0:3],
# - ((r3 @ r1 @ r2)[:,2]) * ((g - f_const)*(1 - tc) / (1 - u[3]) + f_const) + gravity
# )
# xdot
xdot = SX.sym('xdot', 9)
#parameters
p = []
f_impl = xdot - f_expl
model = AcadosModel()
model.f_impl_expr = f_impl
model.f_expl_expr = f_expl
model.x = x
model.xdot = xdot
model.u = u
# model.z = z
model.p = p
model.name = model_name
return model
def acados_linear_quad_model_moving_eq(num_states, g=9.81):
model_name = 'linear_quad_v2'
tc = 0.59375
# scaling_control_mat = np.array([1, 1, 1, (g/tc)])
gravity = SX.zeros(3)
gravity[2] = g
x = SX.sym('x', 9)
u = SX.sym('u', 4)
xdot = SX.sym('xdot', 9)
r1 = SX(3, 3)
r1[0, 0] = 1
r1[1, 1] = cos(x[3])
r1[1, 2] = -sin(x[3])
r1[2, 1] = sin(x[3])
r1[2, 2] = cos(x[3])
r2 = SX(3, 3)
r2[0, 0] = cos(x[4])
r2[0, 2] = sin(x[4])
r2[1, 1] = 1
r2[2, 0] = -sin(x[4])
r2[2, 2] = cos(x[4])
r3 = SX(3, 3)
r3[0, 0] = cos(x[5])
r3[0, 1] = -sin(x[5])
r3[1, 0] = sin(x[5])
r3[1, 1] = cos(x[5])
r3[2, 2] = 1
tau = SX(3, 3)
tau[:, 0] = (inv(r2))[:, 0]
tau[:, 1] = (inv(r2))[:, 1]
tau[:, 2] = (inv(r2 @ r1))[:, 2]
f_nl = vertcat(x[6:9],
inv(tau) @ u[0:3],
-((r3 @ r1 @ r2)[:, 2]) * u[3] * (g / tc) + gravity)
# f_const = 20
# f_nl = vertcat(
# x[6:9],
# inv(tau) @ u[0:3],
# - ((r3 @ r1 @ r2)[:,2]) * (u[3] * ((g + f_const)/tc) - f_const) + gravity
# )
A = jacobian(f_nl, x)
B = jacobian(f_nl, u)
p = []
f_expl = mtimes(A, x) + mtimes(B, u)
f_impl = xdot - f_expl
model = AcadosModel()
model.f_impl_expr = f_impl
model.f_expl_expr = f_expl
model.x = x
model.xdot = xdot
model.u = u
# model.z = z
model.p = p
model.name = model_name
return model
def export_quaternion_ode_model():
model_name = 'quaternion_ode'
# set up states & controls
q0 = SX.sym('q0')
q1 = SX.sym('q1')
q2 = SX.sym('q2')
q3 = SX.sym('q3')
omegax = SX.sym('omegax')
omegay = SX.sym('omegay')
omegaz = SX.sym('omegaz')
x = vertcat(q0, q1, q2, q3, omegax, omegay, omegaz)
# controls
omegax_cont = SX.sym('omegax_cont')
omegay_cont = SX.sym('omegay_cont')
omegaz_cont = SX.sym('omegaz_cont')
u = vertcat(omegax_cont, omegay_cont, omegaz_cont)
# xdot
q0_dot = SX.sym('q0_dot')
q1_dot = SX.sym('q1_dot')
q2_dot = SX.sym('q2_dot')
q3_dot = SX.sym('q3_dot')
omegax_dot = SX.sym('omegax_dot')
omegay_dot = SX.sym('omegay_dot')
omegaz_dot = SX.sym('omegaz_dot')
xdot = vertcat(q0_dot, q1_dot, q2_dot, q3_dot, omegax_dot, omegay_dot,
omegaz_dot)
# algebraic variables
# z = None
# parameters
p = []
# This comes from ref[6] pg 449.
# dq = 1/2 * G(q)' * Ω
S = SX.zeros(3, 4)
S[:, 0] = vertcat(-q1, -q2, -q3)
S[:, 1:4] = q0 * np.eye(3) - skew(vertcat(q1, q2, q3))
print("S:", S, S.shape)
OMG = vertcat(omegax, omegay, omegaz)
# dynamics
f_expl = vertcat(0.5 * mtimes(S.T, OMG), u)
f_impl = xdot - f_expl
model = AcadosModel()
model.f_impl_expr = f_impl
model.f_expl_expr = f_expl
model.x = x
model.xdot = xdot
model.u = u
# model.z = []
model.p = p
model.name = model_name
# Reference generation (quaternion to eul) used in cost function.
Eul = SX(3, 1)
Eul[0, 0] = arctan2(2 * (q0 * q1 + q2 * q3), 1 - 2 * (q1**2 + q2**2))
Eul[1, 0] = arcsin(2 * (q0 * q2 - q3 * q1))
Eul[2, 0] = arctan2(2 * (q0 * q3 + q1 * q2), 1 - 2 * (q2**2 + q3**2))
quat_to_eul = Function("quat_to_eul", [x, u], [Eul])
model.cost_y_expr = vertcat(
Eul, x[4:, :], u) #: CasADi expression for nonlinear least squares
model.cost_y_expr_e = vertcat(
Eul,
x[4:, :]) #: CasADi expression for nonlinear least squares, terminal
model.con_h_expr = q0 * q0 + q1 * q1 + q2 * q2 + q3 * q3
return model
def export_car_ode_model(mb, mw, Iw, Rw):
model_name = 'car_ode'
#system parameters
mt = mb + mw
g = 9.81 # [m/s^2]
# set up states & controls
phi = MX.sym('phi')
l = MX.sym('l')
theta = MX.sym('theta')
phi_dot = MX.sym('phi_dot')
l_dot = MX.sym('l_dot')
theta_dot = MX.sym('theta_dot')
x = vertcat(phi, l, theta,
phi_dot, l_dot, theta_dot)
# controls
tau = MX.sym('tau')
f = MX.sym('f')
u = vertcat(tau, f)
# xdot
phi_ddot = MX.sym('phi_ddot')
l_ddot = MX.sym('l_ddot')
theta_ddot = MX.sym('theta_ddot')
xdot = vertcat(phi_dot, l_dot, theta_dot,
phi_ddot, l_ddot, theta_ddot)
# inertia matrix
M = MX(3, 3)
M[0,0]=Iw + (Rw**2)*mb + (Rw**2)*mw
M[0,1]=Rw*mb*sin(theta)
M[0,2]=Rw*l*mb*cos(theta)
M[1,0]=Rw*mb*sin(theta)
M[1,1]=mb
M[1,2]=0
M[2,0]=Rw*l*mb*cos(theta)
M[2,1]=0
M[2,2]=(l**2)*mb
# coriolis and centrifugal terms
C = MX(3,1)
C[0, 0]=Rw*mb*theta_dot*(2*l_dot*cos(theta) - l*theta_dot*sin(theta))
C[1, 0]=-l*mb*(theta_dot**2)
C[2, 0]=2*l*l_dot*mb*theta_dot
# gravity term
G = MX(3,1)
G[0, 0] = 0
G[1, 0] = g*mb*cos(theta)
G[2, 0] = -g*l*mb*sin(theta)
# define S
S = MX(2,3)
S[0, 0] = 1
S[0, 1] = 0
S[0, 2] = -1
S[1, 0] = 0
S[1, 1] = 1
S[1, 2] = 0
p = vertcat([])
# compute acceleration
ddq = mtimes(inv(M), (mtimes(transpose(S),vertcat(tau, f)) - C - G))
print(ddq.shape)
# dynamics
f_expl = vertcat(phi_dot,
l_dot,
theta_dot,
ddq
)
print(f_expl.shape)
f_impl = xdot - f_expl
model = AcadosModel()
model.f_impl_expr = f_impl
model.f_expl_expr = f_expl
model.x = x
model.xdot = xdot
model.u = u
# algebraic variables
model.z = vertcat([])
# parameters
model.p = p
model.name = model_name
#model.con_h_expr = vertcat( (lambda_z - fabs(lambda_x)),
# (x1_dot-Rw*phi_dot))
#print(f"SHAPE: {model.con_h_expr}")
return model
def export_quad_ode_model():
model_name = 'quad_ode'
# set up states & controls
q0 = SX.sym('q0')
q1 = SX.sym('q1')
q2 = SX.sym('q2')
q3 = SX.sym('q3')
omegax = SX.sym('omegax')
omegay = SX.sym('omegay')
omegaz = SX.sym('omegaz')
x = vertcat(q0, q1, q2, q3, omegax, omegay, omegaz)
# controls
w1 = SX.sym('w1')
w2 = SX.sym('w2')
w3 = SX.sym('w3')
w4 = SX.sym('w4')
u = vertcat(w1, w2, w3, w4)
# xdot
q0_dot = SX.sym('q0_dot')
q1_dot = SX.sym('q1_dot')
q2_dot = SX.sym('q2_dot')
q3_dot = SX.sym('q3_dot')
omegax_dot = SX.sym('omegax_dot')
omegay_dot = SX.sym('omegay_dot')
omegaz_dot = SX.sym('omegaz_dot')
xdot = vertcat(q0_dot, q1_dot, q2_dot, q3_dot, omegax_dot, omegay_dot,
omegaz_dot)
# algebraic variables
# z = None
# parameters
# set up parameters
rho = SX.sym('rho') # air density
A = SX.sym('A') # propeller area
Cl = SX.sym('Cl') # lift coefficient
Cd = SX.sym('Cd') # drag coefficient
m = SX.sym('m') # mass of quad
g = SX.sym('g') # gravity
J1 = SX.sym('J1') # mom inertia
J2 = SX.sym('J2') # mom inertia
J3 = SX.sym('J3') # mom inertia
J = diag(vertcat(J1, J2, J3))
# This comes from ref[6] pg 449.
# dq = 1/2 * G(q)' * Ω
S = SX.zeros(3, 4)
S[:, 0] = vertcat(-q1, -q2, -q3)
S[:, 1:4] = q0 * np.eye(3) - skew(vertcat(q1, q2, q3))
print("S:", S, S.shape)
#Define angular velocity vector
OMG = vertcat(omegax, omegay, omegaz)
p = vertcat(rho, A, Cl, Cd, m, g, J1, J2, J3)
#Define torque applied to the system
T = SX(3, 1)
T[0, 0] = 0.5 * A * Cl * rho * (w2 * w2 - w4 * w4)
T[1, 0] = 0.5 * A * Cl * rho * (w1 * w1 - w3 * w3)
T[2, 0] = 0.5 * A * Cd * rho * (w1 * w1 - w2 * w2 + w3 * w3 - w4 * w4)
# Reference generation (quaternion to eul) used in cost function.
Eul = SX(3, 1)
Eul[0, 0] = arctan2(2 * (q0 * q1 + q2 * q3), 1 - 2 * (q1**2 + q2**2))
Eul[1, 0] = arcsin(2 * (q0 * q2 - q3 * q1))
Eul[2, 0] = arctan2(2 * (q0 * q3 + q1 * q2), 1 - 2 * (q2**2 + q3**2))
# dynamics
f_expl = vertcat(0.5 * mtimes(transpose(S), OMG),
mtimes(inv(J), (T - cross(OMG, mtimes(J, OMG)))))
f_impl = xdot - f_expl
print("dynamics shape: ", f_expl.shape)
model = AcadosModel()
model.f_impl_expr = f_impl
model.f_expl_expr = f_expl
model.x = x
model.xdot = xdot
model.u = u
# model.z = []
model.p = p
model.name = model_name
# add the nonlinear cost.
# Cost defined in paper
# --- ---
# | roll(x) - roll_ref |
# | pitch(x) - pitch_ref |
# l(x,u,z) = | yaw(x) - yaw_ref |
# | x - x_ref |
# | u - u_ref |
# --- --- 14x1
# --- ---
# | roll(x) - roll_ref |
# | pitch(x) - pitch_ref |
# m(x) = | yaw(x) - yaw_ref |
# | x - x_ref |
# --- --- 10x1
model.cost_y_expr = vertcat(
Eul, x, u) #: CasADi expression for nonlinear least squares
model.cost_y_expr_e = vertcat(
Eul, x) #: CasADi expression for nonlinear least squares, terminal
model.con_h_expr = q0 * q0 + q1 * q1 + q2 * q2 + q3 * q3
# # yReference checking.
# refCheck = Function('y_ref', [vertcat(x, u)], [model.cost_y_expr])
# print(refCheck)
# y_ref = refCheck(np.array([0.566, 0.571, 0.168, 0.571, 0.1, 0.2, 0.3, 1, 2, 3, 4]))
# print(y_ref)
#
# dynCheck = Function('sys_dyn', [vertcat(x, u),p], [f_expl])
# print(dynCheck)
# dx_ref = dynCheck(np.array([0.566, 0.571, 0.168, 0.571, 0.1, 0.2, 0.3, 1, 2, 3, 4]),np.array([1, 1, 1, 1, 1, 1, 1, 1, 1]))
# print(dx_ref)
return model
def __init__(self,):
g_ = 9.8066
# control input
roll_rate_ref_ = ca.SX.sym('roll_rate_ref')
pitch_rate_ref_ = ca.SX.sym('pitch_rate_ref')
yaw_rate_ref_ = ca.SX.sym('yaw_rate_ref')
thrust_ref_ = ca.SX.sym('thrust_ref')
controls = ca.vcat([roll_rate_ref_, pitch_rate_ref_, yaw_rate_ref_, thrust_ref_])
# model state
x_ = ca.SX.sym('x')
y_ = ca.SX.sym('y')
z_ = ca.SX.sym('z')
vx_ = ca.SX.sym('vx')
vy_ = ca.SX.sym('vy')
vz_ = ca.SX.sym('vz')
qw_ = ca.SX.sym('qw')
qx_ = ca.SX.sym('qx')
qy_ = ca.SX.sym('qy')
qz_ = ca.SX.sym('qz')
# states [p, q, v]
states = ca.vcat([x_, y_, z_, qw_, qx_, qy_, qz_, vx_, vy_, vz_])
rhs = [
vx_,
vy_,
vz_,
0.5*(-roll_rate_ref_*qx_- pitch_rate_ref_*qy_ - yaw_rate_ref_*qz_),
0.5*(roll_rate_ref_*qw_ + yaw_rate_ref_*qy_ - pitch_rate_ref_*qz_),
0.5*(pitch_rate_ref_*qw_ - yaw_rate_ref_*qx_ + roll_rate_ref_*qz_),
0.5*(yaw_rate_ref_*qw_ + pitch_rate_ref_*qx_ - roll_rate_ref_*qy_),
2*(qw_*qy_ + qx_*qz_) * thrust_ref_,
2*(qy_*qz_ - qw_*qx_) * thrust_ref_,
(qw_*qw_ - qx_*qx_ - qy_*qy_ + qz_*qz_) * thrust_ref_ - g_,
]
self.f = ca.Function('f', [states, controls], [ca.vcat(rhs)])
x_dot = ca.SX.sym('x_dot', len(rhs))
f_impl = x_dot - self.f(states, controls)
model = AcadosModel()
model.f_expl_expr = self.f(states, controls)
model.f_impl_expr = f_impl
model.x = states
model.xdot = x_dot
model.u = controls
model.p = []
model.name = 'quadrotor_q'
constraints = ca.types.SimpleNamespace()
constraints.roll_rate_min = -6.0
constraints.roll_rate_max = 6.0
constraints.pitch_rate_min = -6.0
constraints.pitch_rate_max = 6.0
constraints.yaw_rate_min = -3.14
constraints.yaw_rate_max = 3.14
constraints.thrust_min = 2.0
constraints.thrust_max = g_*1.5
self.model = model
self.constraints = constraints
def acados_linear_quad_model_moving_eq(num_states, g=9.81):
'''
A linear model linearized about the moving point. (I am not certain what I
am linearizing about here actually). I compute the Jacobian at every
execution basically.
'''
model_name = 'linear_quad_v2'
tc = 0.59375
# scaling_control_mat = np.array([1, 1, 1, (g/tc)])
gravity = SX.zeros(3)
gravity[2] = g
x = SX.sym('x', 9)
u = SX.sym('u', 4)
xdot = SX.sym('xdot', 9)
r1 = SX(3, 3)
r1[0, 0] = 1
r1[1, 1] = cos(x[3])
r1[1, 2] = -sin(x[3])
r1[2, 1] = sin(x[3])
r1[2, 2] = cos(x[3])
r2 = SX(3, 3)
r2[0, 0] = cos(x[4])
r2[0, 2] = sin(x[4])
r2[1, 1] = 1
r2[2, 0] = -sin(x[4])
r2[2, 2] = cos(x[4])
r3 = SX(3, 3)
r3[0, 0] = cos(x[5])
r3[0, 1] = -sin(x[5])
r3[1, 0] = sin(x[5])
r3[1, 1] = cos(x[5])
r3[2, 2] = 1
tau = SX(3, 3)
tau[:, 0] = (inv(r2))[:, 0]
tau[:, 1] = (inv(r2))[:, 1]
tau[:, 2] = (inv(r2 @ r1))[:, 2]
f_nl = vertcat(x[6:9],
inv(tau) @ u[0:3],
-((r3 @ r1 @ r2)[:, 2]) * u[3] * (g / tc) + gravity)
A = jacobian(f_nl, x)
B = jacobian(f_nl, u)
p = []
f_expl = mtimes(A, x) + mtimes(B, u)
f_impl = xdot - f_expl
model = AcadosModel()
model.f_impl_expr = f_impl
model.f_expl_expr = f_expl
model.x = x
model.xdot = xdot
model.u = u
# model.z = z
model.p = p
model.name = model_name
return model
def export_car_ode_model(mb, mw, Iw, Rw, l):
model_name = 'car_phase0_ode'
#system parameters
mt = mb + mw
g = 9.81 # [m/s^2]
# set up states & controls
phi = MX.sym('phi')
theta = MX.sym('theta')
phi_dot = MX.sym('phi_dot')
theta_dot = MX.sym('theta_dot')
x = vertcat(phi, theta,
phi_dot, theta_dot)
# controls
tau = MX.sym('tau')
u = vertcat(tau)
# xdot
phi_ddot = MX.sym('phi_ddot')
theta_ddot = MX.sym('theta_ddot')
xdot = vertcat(phi_dot, theta_dot,
phi_ddot, theta_ddot)
phidd = tau/(Iw+mt*Rw*Rw)
# dynamics
f_expl = vertcat(phi_dot,
theta_dot,
phidd,
(1/mb*l*l)*(-mb*Rw*phidd*l*sin(theta) + mb*g*l*cos(theta) - tau)
)
print(f_expl.shape)
f_impl = xdot - f_expl
model = AcadosModel()
model.f_impl_expr = f_impl
model.f_expl_expr = f_expl
model.x = x
model.xdot = xdot
model.u = u
# algebraic variables
model.z = vertcat([])
# parameters
p = vertcat([])
model.p = p
model.name = model_name
# activate1 = MX.sym('activate1')
# activate2 = MX.sym('activate2')
# activate3 = MX.sym('activate3')
# activate4 = MX.sym('activate4')
# activate5 = MX.sym('activate5')
# activate6 = MX.sym('activate6')
# activate7 = MX.sym('activate7')
# model.p = vertcat(activate1, activate2, activate3, activate4, activate5, activate6, activate7)
#model.con_h_expr = vertcat( (lambda_z - fabs(lambda_x)),
# (x1_dot-Rw*phi_dot))
print(f"SHAPE: {model.con_h_expr}")
return model
def acados_settings(Tf, N):
# create render arguments
ocp = AcadosOcp()
# export model
model, constraint = usv_model()
# define acados ODE
model_ac = AcadosModel()
model_ac.f_impl_expr = model.f_impl_expr
model_ac.f_expl_expr = model.f_expl_expr
model_ac.x = model.x
model_ac.xdot = model.xdot
model_ac.u = model.U
model_ac.z = model.z
model_ac.p = model.p
model_ac.name = model.name
ocp.model = model_ac
# define constraint
model_ac.con_h_expr = constraint.expr
# set dimensions
nx = model.x.size()[0]
nu = model.U.size()[0]
ny = nx + nu
ny_e = nx
ocp.dims.N = N
ns = 8
nsh = 8
# set cost
Q = np.diag([0, 0, 0.05, 0.01, 0, 0, 0, 0])
R = np.eye(nu)
R[0, 0] = 0.2
Qe = np.diag([0, 0, 0.1, 0.05, 0, 0, 0, 0])
ocp.cost.cost_type = "LINEAR_LS"
ocp.cost.cost_type_e = "LINEAR_LS"
#unscale = N / Tf
#ocp.cost.W = unscale * scipy.linalg.block_diag(Q, R)
#ocp.cost.W_e = Qe / unscale
ocp.cost.W = scipy.linalg.block_diag(Q, R)
ocp.cost.W_e = Qe
Vx = np.zeros((ny, nx))
Vx[:nx, :nx] = np.eye(nx)
ocp.cost.Vx = Vx
Vu = np.zeros((ny, nu))
Vu[8, 0] = 1.0
ocp.cost.Vu = Vu
Vx_e = np.zeros((ny_e, nx))
Vx_e[:nx, :nx] = np.eye(nx)
ocp.cost.Vx_e = Vx_e
ocp.cost.zl = 10 * np.ones((ns, )) #previously 100
ocp.cost.Zl = 0 * np.ones((ns, ))
ocp.cost.zu = 10 * np.ones((ns, )) #previously 100
ocp.cost.Zu = 0 * np.ones((ns, ))
# set intial references
ocp.cost.yref = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0])
ocp.cost.yref_e = np.array([0, 0, 0, 0, 0, 0, 0, 0])
# setting constraints
#ocp.constraints.lbx = np.array([model.psieddot_min])
#ocp.constraints.ubx = np.array([model.psieddot_max])
#ocp.constraints.idxbx = np.array([8])
ocp.constraints.lbu = np.array([model.Upsieddot_min])
ocp.constraints.ubu = np.array([model.Upsieddot_max])
ocp.constraints.idxbu = np.array([0])
# ocp.constraints.lsbx=np.zero s([1])
# ocp.constraints.usbx=np.zeros([1])
# ocp.constraints.idxsbx=np.array([1])
ocp.constraints.lh = np.array([
constraint.distance_min, constraint.distance_min,
constraint.distance_min, constraint.distance_min,
constraint.distance_min, constraint.distance_min,
constraint.distance_min, constraint.distance_min
])
ocp.constraints.uh = np.array([
1000000, 1000000, 1000000, 1000000, 1000000, 1000000, 1000000, 1000000
])
'''ocp.constraints.lsh = np.zeros(nsh)
ocp.constraints.ush = np.zeros(nsh)
ocp.constraints.idxsh = np.array([0, 2])'''
ocp.constraints.lsh = np.array(
[-0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2, -0.2])
ocp.constraints.ush = np.array([0, 0, 0, 0, 0, 0, 0, 0])
ocp.constraints.idxsh = np.array([0, 1, 2, 3, 4, 5, 6, 7])
# set intial condition
ocp.constraints.x0 = model.x0
#ocp.parameter_values = np.array([4,4,4,8,4,12,4,20,100,100,100,100,100,100,100,100])
ocp.parameter_values = np.array([
100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100,
100, 100
])
# set QP solver and integration
ocp.solver_options.tf = Tf
#ocp.solver_options.qp_solver = 'FULL_CONDENSING_QPOASES'
ocp.solver_options.qp_solver = "PARTIAL_CONDENSING_HPIPM"
ocp.solver_options.nlp_solver_type = "SQP_RTI"
#ocp.solver_options.nlp_solver_type = "SQP"
ocp.solver_options.hessian_approx = "GAUSS_NEWTON"
ocp.solver_options.integrator_type = "ERK"
#ocp.solver_options.sim_method_num_stages = 4
#ocp.solver_options.sim_method_num_steps = 3
#ocp.solver_options.nlp_solver_max_iter = 200
#ocp.solver_options.tol = 1e-4
#ocp.solver_options.qp_solver_tol_stat = 1e-2
#ocp.solver_options.qp_solver_tol_eq = 1e-2
#ocp.solver_options.qp_solver_tol_ineq = 1e-2
#ocp.solver_options.qp_solver_tol_comp = 1e-2
# create solver
acados_solver = AcadosOcpSolver(ocp, json_file="acados_ocp.json")
return constraint, model, acados_solver
def acados_nonlinear_quad_model(num_states, g=9.81):
'''
A non-linear model of the quadrotor. We assume the input
u = [phi_dot,theta_dot,psi_dot,throttle]. Therefore, the system has only 9
states.
'''
model_name = 'nonlinear_quad'
position = SX.sym('o', 3)
velocity = SX.sym('v', 3)
phi = SX.sym('phi')
theta = SX.sym('theta')
psi = SX.sym('psi')
gravity = SX.zeros(3)
gravity[2] = g
x = vertcat(position, phi, theta, psi, velocity)
p = SX.sym('p')
q = SX.sym('q')
r = SX.sym('r')
F = SX.sym('F')
u = vertcat(p, q, r, F)
r1 = SX(3, 3)
r1[0, 0] = 1
r1[1, 1] = cos(phi)
r1[1, 2] = -sin(phi)
r1[2, 1] = sin(phi)
r1[2, 2] = cos(phi)
r2 = SX(3, 3)
r2[0, 0] = cos(theta)
r2[0, 2] = sin(theta)
r2[1, 1] = 1
r2[2, 0] = -sin(theta)
r2[2, 2] = cos(theta)
r3 = SX(3, 3)
r3[0, 0] = cos(psi)
r3[0, 1] = -sin(psi)
r3[1, 0] = sin(psi)
r3[1, 1] = cos(psi)
r3[2, 2] = 1
tau = SX(3, 3)
tau[:, 0] = (inv(r2))[:, 0]
tau[:, 1] = (inv(r2))[:, 1]
tau[:, 2] = (inv(r2 @ r1))[:, 2]
# tau[:,0] = (inv(r1))[:,0]
# tau[:,1] = (inv(r1))[:,1]
# tau[:,2] = (inv(r1 @ r2))[:,2]
# tau[0,0] = cos(theta)
# tau[0,2] = -sin(theta)
# tau[1,1] = 1
# tau[1,2] = sin(phi) * cos(theta)
# tau[2,0] = sin(theta)
# tau[2,2] = cos(phi) * cos(theta)
tc = 0.59375 # mapping control to throttle
# tc = 0.75
# scaling_control_mat = np.array([1, 1, 1, (g/tc)])
f_expl = vertcat(velocity,
inv(tau) @ u[0:3],
-((r3 @ r1 @ r2)[:, 2]) * u[3] * (g / tc) + gravity)
# xdot
xdot = SX.sym('xdot', 9)
#parameters
p = []
f_impl = xdot - f_expl
model = AcadosModel()
model.f_impl_expr = f_impl
model.f_expl_expr = f_expl
model.x = x
model.xdot = xdot
model.u = u
# model.z = z
model.p = p
model.name = model_name
return model
def generate(self, dae=None, quad=None, name='tunempc', opts={}):
""" Create embeddable NLP solver
"""
from acados_template import AcadosModel, AcadosOcp, AcadosOcpSolver, AcadosSimSolver
# extract dimensions
nx = self.__nx
nu = self.__nu + self.__ns # treat slacks as pseudo-controls
# extract reference
ref = self.__ref
xref = np.squeeze(self.__ref[0][:nx], axis=1)
uref = np.squeeze(self.__ref[0][nx:nx + nu], axis=1)
# sampling time
self.__ts = opts['tf'] / self.__N
# create acados model
model = AcadosModel()
model.x = ca.MX.sym('x', nx)
model.u = ca.MX.sym('u', nu)
model.p = []
model.name = name
# detect input type
if dae is None:
model.f_expl_expr = self.__F(x0=model.x,
p=model.u)['xf'] / self.__ts
opts['integrator_type'] = 'ERK'
opts['sim_method_num_stages'] = 1
opts['sim_method_num_steps'] = 1
else:
n_in = dae.n_in()
if n_in == 2:
# xdot = f(x, u)
if 'integrator_type' in opts:
if opts['integrator_type'] in ['IRK', 'GNSF']:
xdot = ca.MX.sym('xdot', nx)
model.xdot = xdot
model.f_impl_expr = xdot - dae(model.x,
model.u[:self.__nu])
model.f_expl_expr = xdot
elif opts['integrator_type'] == 'ERK':
model.f_expl_expr = dae(model.x, model.u[:self.__nu])
else:
raise ValueError('Provide numerical integrator type!')
else:
xdot = ca.MX.sym('xdot', nx)
model.xdot = xdot
model.f_expl_expr = xdot
if n_in == 3:
# f(xdot, x, u) = 0
model.f_impl_expr = dae(xdot, model.x, model.u[:self.__nu])
elif n_in == 4:
# f(xdot, x, u, z) = 0
nz = dae.size1_in(3)
z = ca.MX.sym('z', nz)
model.z = z
model.f_impl_expr = dae(xdot, model.x, model.u[:self.__nu],
z)
else:
raise ValueError(
'Invalid number of inputs for system dynamics function.'
)
if self.__gnl is not None:
model.con_h_expr = self.__gnl(model.x, model.u[:self.__nu],
model.u[self.__nu:])
if self.__type == 'economic':
if quad is None:
model.cost_expr_ext_cost = self.__cost(
model.x, model.u[:self.__nu]) / self.__ts
else:
model.cost_expr_ext_cost = self.__cost(model.x,
model.u[:self.__nu])
# create acados ocp
ocp = AcadosOcp()
ocp.model = model
ny = nx + nu
ny_e = nx
if 'integrator_type' in opts and opts['integrator_type'] == 'GNSF':
from acados_template import acados_dae_model_json_dump
import os
acados_dae_model_json_dump(model)
# Set up Octave to be able to run the following:
## if using a virtual python env, the following lines can be added to the env/bin/activate script:
# export OCTAVE_PATH=$OCTAVE_PATH:$ACADOS_INSTALL_DIR/external/casadi-octave
# export OCTAVE_PATH=$OCTAVE_PATH:$ACADOS_INSTALL_DIR/interfaces/acados_matlab_octave/
# export OCTAVE_PATH=$OCTAVE_PATH:$ACADOS_INSTALL_DIR/interfaces/acados_matlab_octave/acados_template_mex/
# echo
# echo "OCTAVE_PATH=$OCTAVE_PATH"
status = os.system(
"octave --eval \"convert({})\"".format("\'" + model.name +
"_acados_dae.json\'"))
# load gnsf from json
with open(model.name + '_gnsf_functions.json', 'r') as f:
import json
gnsf_dict = json.load(f)
ocp.gnsf_model = gnsf_dict
# set horizon length
ocp.dims.N = self.__N
# set cost module
if self.__type == 'economic':
# set cost function type to external (provided in model)
ocp.cost.cost_type = 'EXTERNAL'
if quad is not None:
ocp.solver_options.cost_discretization = 'INTEGRATOR'
elif self.__type == 'tracking':
# set weighting matrices
ocp.cost.W = self.__Href[0][0]
# set-up linear least squares cost
ocp.cost.cost_type = 'LINEAR_LS'
ocp.cost.W_e = np.zeros((nx, nx))
ocp.cost.Vx = np.zeros((ny, nx))
ocp.cost.Vx[:nx, :nx] = np.eye(nx)
Vu = np.zeros((ny, nu))
Vu[nx:, :] = np.eye(nu)
ocp.cost.Vu = Vu
ocp.cost.Vx_e = np.eye(nx)
ocp.cost.yref = np.squeeze(
ca.vertcat(xref,uref).full() - \
ct.mtimes(np.linalg.inv(ocp.cost.W),self.__qref[0][0].T).full(), # gradient term
axis = 1
)
ocp.cost.yref_e = np.zeros((ny_e, ))
if n_in == 4: # DAE flag
ocp.cost.Vz = np.zeros((ny, nz))
# if 'custom_hessian' in opts:
# self.__custom_hessian = opts['custom_hessian']
# initial condition
ocp.constraints.x0 = xref
# set inequality constraints
ocp.constraints.constr_type = 'BGH'
if self.__S['C'] is not None:
C = self.__S['C'][0][:, :nx]
D = self.__S['C'][0][:, nx:]
lg = -self.__S['e'][0] + ct.mtimes(C, xref).full() + ct.mtimes(
D, uref).full()
ug = 1e8 - self.__S['e'][0] + ct.mtimes(
C, xref).full() + ct.mtimes(D, uref).full()
ocp.constraints.lg = np.squeeze(lg, axis=1)
ocp.constraints.ug = np.squeeze(ug, axis=1)
ocp.constraints.C = C
ocp.constraints.D = D
if 'usc' in self.__vars:
if 'us' in self.__vars:
arg = [
self.__vars['x'], self.__vars['u'], self.__vars['us'],
self.__vars['usc']
]
else:
arg = [
self.__vars['x'], self.__vars['u'], self.__vars['usc']
]
Jsg = ca.Function(
'Jsg', [self.__vars['usc']],
[ca.jacobian(self.__h(*arg), self.__vars['usc'])])(0.0)
self.__Jsg = Jsg.full()[:-self.__nsc, :]
ocp.constraints.Jsg = self.__Jsg
ocp.cost.Zl = np.zeros((self.__nsc, ))
ocp.cost.Zu = np.zeros((self.__nsc, ))
ocp.cost.zl = np.squeeze(self.__scost.full(),
axis=1) / self.__ts
ocp.cost.zu = np.squeeze(self.__scost.full(),
axis=1) / self.__ts
# set nonlinear equality constraints
if self.__gnl is not None:
ocp.constraints.lh = np.zeros(self.__ns, )
ocp.constraints.uh = np.zeros(self.__ns, )
# terminal constraint:
x_term = self.__p_operator(model.x)
Jbx = ca.Function('Jbx', [model.x],
[ca.jacobian(x_term, model.x)])(0.0)
ocp.constraints.Jbx_e = Jbx.full()
ocp.constraints.lbx_e = np.squeeze(self.__p_operator(xref).full(),
axis=1)
ocp.constraints.ubx_e = np.squeeze(self.__p_operator(xref).full(),
axis=1)
for option in list(opts.keys()):
if hasattr(ocp.solver_options, option):
setattr(ocp.solver_options, option, opts[option])
self.__acados_ocp_solver = AcadosOcpSolver(ocp,
json_file='acados_ocp_' +
model.name + '.json')
self.__acados_integrator = AcadosSimSolver(ocp,
json_file='acados_ocp_' +
model.name + '.json')
# set initial guess
self.__set_acados_initial_guess()
return self.__acados_ocp_solver, self.__acados_integrator
def export_car_ode_model(mb, mw, Iw, Rw):
model_name = 'car_ode'
#system parameters
mt = mb + mw
g = 9.81 # [m/s^2]
# set up states & controls
x1 = MX.sym('x1')
z = MX.sym('z')
phi = MX.sym('phi')
l = MX.sym('l')
theta = MX.sym('theta')
x1_dot = MX.sym('x1_dot')
z_dot = MX.sym('z_dot')
phi_dot = MX.sym('phi_dot')
l_dot = MX.sym('l_dot')
theta_dot = MX.sym('theta_dot')
x = vertcat(x1, z, phi, l, theta, x1_dot, z_dot, phi_dot, l_dot, theta_dot)
# controls
tau = MX.sym('tau')
f = MX.sym('f')
lambda_x = MX.sym('lambda_x')
lambda_z = MX.sym('lambda_z')
u = vertcat(tau, f, lambda_x, lambda_z)
# xdot
x1_ddot = MX.sym('x1_ddot')
z_ddot = MX.sym('z_ddot')
phi_ddot = MX.sym('phi_ddot')
l_ddot = MX.sym('l_ddot')
theta_ddot = MX.sym('theta_ddot')
xdot = vertcat(x1_dot, z_dot, phi_dot, l_dot, theta_dot, x1_ddot, z_ddot,
phi_ddot, l_ddot, theta_ddot)
# inertia matrix
M = MX(5, 5)
M[0, 0] = mt
M[0, 1] = 0
M[0, 2] = 0
M[0, 3] = mb * sin(theta)
M[0, 4] = mb * l * cos(theta)
M[1, 0] = 0
M[1, 1] = mt
M[1, 2] = 0
M[1, 3] = mb * cos(theta)
M[1, 4] = -mb * l * sin(theta)
M[2, 0] = 0
M[2, 1] = 0
M[2, 2] = Iw
M[2, 3] = 0
M[2, 4] = 0
M[3, 0] = mb * sin(theta)
M[3, 1] = mb * cos(theta)
M[3, 2] = 0
M[3, 3] = mb
M[3, 4] = 0
M[4, 0] = mb * l * cos(theta)
M[4, 1] = -mb * l * sin(theta)
M[4, 2] = 0
M[4, 3] = 0
M[4, 4] = mb * (l**2)
# coriolis and centrifugal terms
C = MX(5, 5)
C[0, 0] = 0
C[0, 1] = 0
C[0, 2] = 0
C[0, 3] = 2 * mb * cos(theta) * theta_dot
C[0, 4] = -mb * l * sin(theta) * theta_dot
C[1, 0] = 0
C[1, 1] = 0
C[1, 2] = 0
C[1, 3] = -2 * mb * sin(theta) * theta_dot
C[1, 4] = -mb * l * cos(theta) * theta_dot
C[2, 0] = 0
C[2, 1] = 0
C[2, 2] = 0
C[2, 3] = 0
C[2, 4] = 0
C[3, 0] = 0
C[3, 1] = 0
C[3, 2] = 0
C[3, 3] = 0
C[3, 4] = -mb * l * theta_dot
C[4, 0] = 0
C[4, 1] = 0
C[4, 2] = 0
C[4, 3] = 2 * mb * l * theta_dot
C[4, 4] = 0
# gravity term
G = MX(5, 1)
G[0, 0] = 0
G[1, 0] = g * mt
G[2, 0] = 0
G[3, 0] = g * mb * cos(theta)
G[4, 0] = -g * mb * l * sin(theta)
# define S
S = MX(2, 5)
S[0, 0] = 0
S[0, 1] = 0
S[0, 2] = 1
S[0, 3] = 0
S[0, 4] = -1
S[1, 0] = 0
S[1, 1] = 0
S[1, 2] = 0
S[1, 3] = 1
S[1, 4] = 0
# define Jc
Jc = MX(2, 5)
Jc[0, 0] = 1
Jc[0, 1] = 0
Jc[0, 2] = -Rw
Jc[0, 3] = 0
Jc[0, 4] = 0
Jc[1, 0] = 0
Jc[1, 1] = 1
Jc[1, 2] = 0
Jc[1, 3] = 0
Jc[1, 4] = 0
# compute acceleration
ddq = mtimes(
inv(M),
(mtimes(transpose(S), vertcat(tau, f)) +
mtimes(transpose(Jc), vertcat(lambda_x, lambda_z)) -
mtimes(C, vertcat(x1_dot, z_dot, phi_dot, l_dot, theta_dot)) - G))
print(ddq.shape)
# dynamics
f_expl = vertcat(x1_dot, z_dot, phi_dot, l_dot, theta_dot, ddq)
print(f_expl.shape)
f_impl = xdot - f_expl
model = AcadosModel()
model.f_impl_expr = f_impl
model.f_expl_expr = f_expl
model.x = x
model.xdot = xdot
model.u = u
# algebraic variables
model.z = vertcat([])
# parameters
p = vertcat([])
model.p = p
model.name = model_name
# activate1 = MX.sym('activate1')
# activate2 = MX.sym('activate2')
# activate3 = MX.sym('activate3')
# activate4 = MX.sym('activate4')
# activate5 = MX.sym('activate5')
# activate6 = MX.sym('activate6')
# activate7 = MX.sym('activate7')
# model.p = vertcat(activate1, activate2, activate3, activate4, activate5, activate6, activate7)
model.con_h_expr = vertcat((lambda_z - fabs(lambda_x)),
(x1_dot - Rw * phi_dot))
print(f"SHAPE: {model.con_h_expr}")
return model
def acados_settings_dyn(Tf, N, modelparams = "modelparams.yaml"):
#create render arguments
ocp = AcadosOcp()
#load model
model, constraints = dynamic_model(modelparams)
# define acados ODE
model_ac = AcadosModel()
model_ac.f_impl_expr = model.f_impl_expr
model_ac.f_expl_expr = model.f_expl_expr
model_ac.x = model.x
model_ac.xdot = model.xdot
#inputvector
model_ac.u = model.u
#parameter vector
model_ac.p = model.p
#parameter vector
model_ac.z = model.z
#external cost function
model_ac.cost_expr_ext_cost = model.stage_cost
#model_ac.cost_expr_ext_cost_e = 0
model_ac.con_h_expr = model.con_h_expr
model_ac.name = model.name
ocp.model = model_ac
# set dimensions
nx = model.x.size()[0]
nu = model.u.size()[0]
nz = model.z.size()[0]
np = model.p.size()[0]
#ny = nu + nx
#ny_e = nx
ocp.dims.nx = nx
ocp.dims.nz = nz
#ocp.dims.ny = ny
#ocp.dims.ny_e = ny_e
ocp.dims.nu = nu
ocp.dims.np = np
ocp.dims.nh = 1
#ocp.dims.ny = ny
#ocp.dims.ny_e = ny_e
ocp.dims.nbx = 3
ocp.dims.nsbx = 0
ocp.dims.nbu = nu
#number of soft on h constraints
ocp.dims.nsh = 1
ocp.dims.ns = 1
ocp.dims.N = N
# set cost to casadi expression defined above
ocp.cost.cost_type = "EXTERNAL"
#ocp.cost.cost_type_e = "EXTERNAL"
ocp.cost.zu = 1000 * npy.ones((ocp.dims.ns,))
ocp.cost.Zu = 1000 * npy.ones((ocp.dims.ns,))
ocp.cost.zl = 1000 * npy.ones((ocp.dims.ns,))
ocp.cost.Zl = 1000 * npy.ones((ocp.dims.ns,))
#not sure if needed
#unscale = N / Tf
#constraints
#stagewise constraints for tracks with slack
ocp.constraints.uh = npy.array([0.00])
ocp.constraints.lh = npy.array([-10])
ocp.constraints.lsh = 0.1*npy.ones(ocp.dims.nsh)
ocp.constraints.ush = -0.1*npy.ones(ocp.dims.nsh)
ocp.constraints.idxsh = npy.array([0])
#ocp.constraints.Jsh = 1
# boxconstraints
# xvars = ['posx', 'posy', 'phi', 'vx', 'vy', 'omega', 'd', 'delta', 'theta']
ocp.constraints.lbx = npy.array([10, 10, 100, model.vx_min, model.vy_min, model.omega_min, model.d_min, model.delta_min, model.theta_min])
ocp.constraints.ubx = npy.array([10, 10, 100, model.vx_max, model.vy_max, model.omega_max, model.d_max, model.delta_max, model.theta_max])
ocp.constraints.idxbx = npy.arange(nx)
#ocp.constraints.lsbx= -0.1 * npy.ones(ocp.dims.nbx)
#ocp.constraints.usbx= 0.1 * npy.ones(ocp.dims.nbx)
#ocp.constraints.idxsbx= npy.array([6,7,8])
#print("ocp.constraints.idxbx: ",ocp.constraints.idxbx)
ocp.constraints.lbu = npy.array([model.ddot_min, model.deltadot_min, model.thetadot_min])
ocp.constraints.ubu = npy.array([model.ddot_max, model.deltadot_max, model.thetadot_max])
ocp.constraints.idxbu = npy.array([0, 1, 2])
# set intial condition
ocp.constraints.x0 = model.x0
# set QP solver and integration
ocp.solver_options.tf = Tf
# ocp.solver_options.qp_solver = 'FULL_CONDENSING_QPOASES'
ocp.solver_options.qp_solver = "PARTIAL_CONDENSING_HPIPM"
ocp.solver_options.nlp_solver_type = "SQP"#"SQP_RTI" #
ocp.solver_options.hessian_approx = "GAUSS_NEWTON"
ocp.solver_options.integrator_type = "ERK"
ocp.parameter_values = npy.zeros(np)
#ocp.solver_options.sim_method_num_stages = 4
#ocp.solver_options.sim_method_num_steps = 3
ocp.solver_options.nlp_solver_step_length = 0.01
ocp.solver_options.nlp_solver_max_iter = 50
ocp.solver_options.tol = 1e-4
#ocp.solver_options.print_level = 1
# ocp.solver_options.nlp_solver_tol_comp = 1e-1
# create solver
acados_solver = AcadosOcpSolver(ocp, json_file="acados_ocp_dynamic.json")
return constraints, model, acados_solver, ocp
def acados_settings(Tf, N, track_file):
# create render arguments
ocp = AcadosOcp()
# export model
model, constraint = bycicle_model(track_file)
# define acados ODE
model_ac = AcadosModel()
model_ac.f_impl_expr = model.f_impl_expr
model_ac.f_expl_expr = model.f_expl_expr
model_ac.x = model.x
model_ac.xdot = model.xdot
model_ac.u = model.u
model_ac.z = model.z
model_ac.p = model.p
model_ac.name = model.name
ocp.model = model_ac
# define constraint
model_ac.con_h_expr = constraint.expr
# set dimensions
nx = model.x.size()[0]
nu = model.u.size()[0]
ny = nx + nu
ny_e = nx
ocp.dims.N = N
ns = 2
nsh = 2
# set cost
Q = np.diag([1e-1, 1e-8, 1e-8, 1e-8, 1e-3, 5e-3])
R = np.eye(nu)
R[0, 0] = 1e-3
R[1, 1] = 5e-3
Qe = np.diag([5e0, 1e1, 1e-8, 1e-8, 5e-3, 2e-3])
ocp.cost.cost_type = "LINEAR_LS"
ocp.cost.cost_type_e = "LINEAR_LS"
unscale = N / Tf
ocp.cost.W = unscale * scipy.linalg.block_diag(Q, R)
ocp.cost.W_e = Qe / unscale
Vx = np.zeros((ny, nx))
Vx[:nx, :nx] = np.eye(nx)
ocp.cost.Vx = Vx
Vu = np.zeros((ny, nu))
Vu[6, 0] = 1.0
Vu[7, 1] = 1.0
ocp.cost.Vu = Vu
Vx_e = np.zeros((ny_e, nx))
Vx_e[:nx, :nx] = np.eye(nx)
ocp.cost.Vx_e = Vx_e
ocp.cost.zl = 100 * np.ones((ns, ))
ocp.cost.Zl = 0 * np.ones((ns, ))
ocp.cost.zu = 100 * np.ones((ns, ))
ocp.cost.Zu = 0 * np.ones((ns, ))
# set intial references
ocp.cost.yref = np.array([1, 0, 0, 0, 0, 0, 0, 0])
ocp.cost.yref_e = np.array([0, 0, 0, 0, 0, 0])
# setting constraints
ocp.constraints.lbx = np.array([-12])
ocp.constraints.ubx = np.array([12])
ocp.constraints.idxbx = np.array([1])
ocp.constraints.lbu = np.array([model.dthrottle_min, model.ddelta_min])
ocp.constraints.ubu = np.array([model.dthrottle_max, model.ddelta_max])
ocp.constraints.idxbu = np.array([0, 1])
# ocp.constraints.lsbx=np.zero s([1])
# ocp.constraints.usbx=np.zeros([1])
# ocp.constraints.idxsbx=np.array([1])
ocp.constraints.lh = np.array([
constraint.along_min,
constraint.alat_min,
model.n_min,
model.throttle_min,
model.delta_min,
])
ocp.constraints.uh = np.array([
constraint.along_max,
constraint.alat_max,
model.n_max,
model.throttle_max,
model.delta_max,
])
ocp.constraints.lsh = np.zeros(nsh)
ocp.constraints.ush = np.zeros(nsh)
ocp.constraints.idxsh = np.array([0, 2])
# set intial condition
ocp.constraints.x0 = model.x0
# set QP solver and integration
ocp.solver_options.tf = Tf
# ocp.solver_options.qp_solver = 'FULL_CONDENSING_QPOASES'
ocp.solver_options.qp_solver = "PARTIAL_CONDENSING_HPIPM"
ocp.solver_options.nlp_solver_type = "SQP_RTI"
ocp.solver_options.hessian_approx = "GAUSS_NEWTON"
ocp.solver_options.integrator_type = "ERK"
ocp.solver_options.sim_method_num_stages = 4
ocp.solver_options.sim_method_num_steps = 3
# ocp.solver_options.qp_solver_tol_stat = 1e-2
# ocp.solver_options.qp_solver_tol_eq = 1e-2
# ocp.solver_options.qp_solver_tol_ineq = 1e-2
# ocp.solver_options.qp_solver_tol_comp = 1e-2
# create solver
acados_solver = AcadosOcpSolver(ocp, json_file="acados_ocp.json")
return constraint, model, acados_solver
def main(use_cython=True):
# (very) simple crane model
beta = 0.001
k = 0.9
a_max = 10
dt_max = 2.0
# states
p1 = SX.sym('p1')
v1 = SX.sym('v1')
p2 = SX.sym('p2')
v2 = SX.sym('v2')
x = vertcat(p1, v1, p2, v2)
# controls
a = SX.sym('a')
dt = SX.sym('dt')
u = vertcat(a, dt)
f_expl = dt * vertcat(v1, a, v2, -beta * v2 - k * (p2 - p1))
model = AcadosModel()
model.f_expl_expr = f_expl
model.x = x
model.u = u
model.name = 'crane_time_opt'
# create ocp object to formulate the OCP
x0 = np.array([2.0, 0.0, 2.0, 0.0])
xf = np.array([0.0, 0.0, 0.0, 0.0])
ocp = AcadosOcp()
ocp.model = model
# N - maximum number of bangs
N = 7
Tf = N
nx = model.x.size()[0]
nu = model.u.size()[0]
# set dimensions
ocp.dims.N = N
# set cost
ocp.cost.cost_type = 'EXTERNAL'
ocp.cost.cost_type_e = 'EXTERNAL'
ocp.model.cost_expr_ext_cost = dt
ocp.model.cost_expr_ext_cost_e = 0
ocp.constraints.lbu = np.array([-a_max, 0.0])
ocp.constraints.ubu = np.array([+a_max, dt_max])
ocp.constraints.idxbu = np.array([0, 1])
ocp.constraints.x0 = x0
ocp.constraints.lbx_e = xf
ocp.constraints.ubx_e = xf
ocp.constraints.idxbx_e = np.array([0, 1, 2, 3])
# set prediction horizon
ocp.solver_options.tf = Tf
# set options
ocp.solver_options.qp_solver = 'FULL_CONDENSING_QPOASES' #'PARTIAL_CONDENSING_HPIPM' # FULL_CONDENSING_QPOASES
ocp.solver_options.integrator_type = 'ERK'
ocp.solver_options.print_level = 3
ocp.solver_options.nlp_solver_type = 'SQP' # SQP_RTI, SQP
ocp.solver_options.globalization = 'MERIT_BACKTRACKING'
ocp.solver_options.nlp_solver_max_iter = 5000
ocp.solver_options.nlp_solver_tol_stat = 1e-6
ocp.solver_options.levenberg_marquardt = 0.1
ocp.solver_options.sim_method_num_steps = 15
ocp.solver_options.qp_solver_iter_max = 100
ocp.code_export_directory = 'c_generated_code'
ocp.solver_options.hessian_approx = 'EXACT'
ocp.solver_options.exact_hess_constr = 0
ocp.solver_options.exact_hess_dyn = 0
if use_cython:
AcadosOcpSolver.generate(ocp, json_file='acados_ocp.json')
AcadosOcpSolver.build(ocp.code_export_directory, with_cython=True)
ocp_solver = AcadosOcpSolver.create_cython_solver('acados_ocp.json')
else: # ctypes
## Note: skip generate and build assuming this is done before (in cython run)
ocp_solver = AcadosOcpSolver(ocp,
json_file='acados_ocp.json',
build=False,
generate=False)
ocp_solver.reset()
for i, tau in enumerate(np.linspace(0, 1, N)):
ocp_solver.set(i, 'x', (1 - tau) * x0 + tau * xf)
ocp_solver.set(i, 'u', np.array([0.1, 0.5]))
simX = np.zeros((N + 1, nx))
simU = np.zeros((N, nu))
status = ocp_solver.solve()
if status != 0:
ocp_solver.print_statistics()
raise Exception(f'acados returned status {status}.')
# get solution
for i in range(N):
simX[i, :] = ocp_solver.get(i, "x")
simU[i, :] = ocp_solver.get(i, "u")
simX[N, :] = ocp_solver.get(N, "x")
dts = simU[:, 1]
print(
"acados solved OCP successfully, creating integrator to simulate the solution"
)
# simulate on finer grid
sim = AcadosSim()
# set model
sim.model = model
# set options
sim.solver_options.integrator_type = 'ERK'
sim.solver_options.num_stages = 4
sim.solver_options.num_steps = 3
sim.solver_options.T = 1.0 # dummy value
dt_approx = 0.0005
dts_fine = np.zeros((N, ))
Ns_fine = np.zeros((N, ), dtype='int16')
# compute number of simulation steps for bang interval + dt_fine
for i in range(N):
N_approx = max(int(dts[i] / dt_approx), 1)
dts_fine[i] = dts[i] / N_approx
Ns_fine[i] = int(round(dts[i] / dts_fine[i]))
N_fine = int(np.sum(Ns_fine))
simU_fine = np.zeros((N_fine, nu))
ts_fine = np.zeros((N_fine + 1, ))
simX_fine = np.zeros((N_fine + 1, nx))
simX_fine[0, :] = x0
acados_integrator = AcadosSimSolver(sim)
k = 0
for i in range(N):
u = simU[i, 0]
acados_integrator.set("u", np.hstack((u, np.ones(1, ))))
# set simulation time
acados_integrator.set("T", dts_fine[i])
for j in range(Ns_fine[i]):
acados_integrator.set("x", simX_fine[k, :])
status = acados_integrator.solve()
if status != 0:
raise Exception(f'acados returned status {status}.')
simX_fine[k + 1, :] = acados_integrator.get("x")
simU_fine[k, :] = u
ts_fine[k + 1] = ts_fine[k] + dts_fine[i]
k += 1
# visualize
if os.environ.get('ACADOS_ON_TRAVIS'):
plt.figure()
state_labels = ['p1', 'v1', 'p2', 'v2']
for i, l in enumerate(state_labels):
plt.subplot(5, 1, i + 1)
plt.plot(ts_fine, simX_fine[:, i], label='time optimal solution')
plt.grid(True)
plt.ylabel(l)
if i == 0:
plt.legend(loc=1)
plt.subplot(5, 1, 5)
plt.step(ts_fine,
np.hstack((simU_fine[:, 0], simU_fine[-1, 0])),
'-',
where='post')
plt.grid(True)
plt.ylabel('a')
plt.xlabel('t')
plt.show()
def solve_marathos_problem_with_setting(setting):
globalization = setting['globalization']
line_search_use_sufficient_descent = setting[
'line_search_use_sufficient_descent']
globalization_use_SOC = setting['globalization_use_SOC']
# create ocp object to formulate the OCP
ocp = AcadosOcp()
# set model
model = AcadosModel()
x1 = SX.sym('x1')
x2 = SX.sym('x2')
x = vertcat(x1, x2)
# dynamics: identity
model.disc_dyn_expr = x
model.x = x
model.u = SX.sym('u', 0, 0) # [] / None doesnt work
model.p = []
model.name = f'marathos_problem'
ocp.model = model
# discretization
Tf = 1
N = 1
ocp.dims.N = N
ocp.solver_options.tf = Tf
# cost
ocp.cost.cost_type_e = 'EXTERNAL'
ocp.model.cost_expr_ext_cost_e = x1
# constarints
ocp.model.con_h_expr = x1**2 + x2**2
ocp.constraints.lh = np.array([1.0])
ocp.constraints.uh = np.array([1.0])
# # soften
# ocp.constraints.idxsh = np.array([0])
# ocp.cost.zl = 1e5 * np.array([1])
# ocp.cost.zu = 1e5 * np.array([1])
# ocp.cost.Zl = 1e5 * np.array([1])
# ocp.cost.Zu = 1e5 * np.array([1])
# add bounds on x
# nx = 2
# ocp.constraints.idxbx_0 = np.array(range(nx))
# ocp.constraints.lbx_0 = -2 * np.ones((nx))
# ocp.constraints.ubx_0 = 2 * np.ones((nx))
# set options
ocp.solver_options.qp_solver = 'PARTIAL_CONDENSING_HPIPM' # FULL_CONDENSING_QPOASES
# PARTIAL_CONDENSING_HPIPM, FULL_CONDENSING_QPOASES, FULL_CONDENSING_HPIPM,
# PARTIAL_CONDENSING_QPDUNES, PARTIAL_CONDENSING_OSQP
ocp.solver_options.hessian_approx = 'EXACT'
ocp.solver_options.integrator_type = 'DISCRETE'
# ocp.solver_options.print_level = 1
ocp.solver_options.tol = TOL
ocp.solver_options.nlp_solver_type = 'SQP' # SQP_RTI, SQP
ocp.solver_options.globalization = globalization
ocp.solver_options.alpha_min = 1e-2
# ocp.solver_options.__initialize_t_slacks = 0
# ocp.solver_options.regularize_method = 'CONVEXIFY'
ocp.solver_options.levenberg_marquardt = 1e-1
# ocp.solver_options.print_level = 2
SQP_max_iter = 300
ocp.solver_options.qp_solver_iter_max = 400
ocp.solver_options.regularize_method = 'MIRROR'
# ocp.solver_options.exact_hess_constr = 0
ocp.solver_options.line_search_use_sufficient_descent = line_search_use_sufficient_descent
ocp.solver_options.globalization_use_SOC = globalization_use_SOC
ocp.solver_options.eps_sufficient_descent = 1e-1
ocp.solver_options.qp_tol = 5e-7
if FOR_LOOPING: # call solver in for loop to get all iterates
ocp.solver_options.nlp_solver_max_iter = 1
ocp_solver = AcadosOcpSolver(ocp, json_file=f'{model.name}.json')
else:
ocp.solver_options.nlp_solver_max_iter = SQP_max_iter
ocp_solver = AcadosOcpSolver(ocp, json_file=f'{model.name}.json')
# initialize solver
rad_init = 0.1 #0.1 #np.pi / 4
xinit = np.array([np.cos(rad_init), np.sin(rad_init)])
# xinit = np.array([0.82120912, 0.58406911])
[ocp_solver.set(i, "x", xinit) for i in range(N + 1)]
# solve
if FOR_LOOPING: # call solver in for loop to get all iterates
iterates = np.zeros((SQP_max_iter + 1, 2))
iterates[0, :] = xinit
alphas = np.zeros((SQP_max_iter, ))
qp_iters = np.zeros((SQP_max_iter, ))
iter = SQP_max_iter
residuals = np.zeros((4, SQP_max_iter))
# solve
for i in range(SQP_max_iter):
status = ocp_solver.solve()
ocp_solver.print_statistics(
) # encapsulates: stat = ocp_solver.get_stats("statistics")
# print(f'acados returned status {status}.')
iterates[i + 1, :] = ocp_solver.get(0, "x")
if status in [0, 4]:
iter = i
break
alphas[i] = ocp_solver.get_stats('alpha')[1]
qp_iters[i] = ocp_solver.get_stats('qp_iter')[1]
residuals[:, i] = ocp_solver.get_stats('residuals')
else:
ocp_solver.solve()
ocp_solver.print_statistics()
iter = ocp_solver.get_stats('sqp_iter')[0]
alphas = ocp_solver.get_stats('alpha')[1:]
qp_iters = ocp_solver.get_stats('qp_iter')
residuals = ocp_solver.get_stats('statistics')[1:5, 1:iter]
# get solution
solution = ocp_solver.get(0, "x")
# print summary
print(f"solved Marathos test problem with settings {setting}")
print(
f"cost function value = {ocp_solver.get_cost()} after {iter} SQP iterations"
)
print(f"alphas: {alphas[:iter]}")
print(f"total number of QP iterations: {sum(qp_iters[:iter])}")
max_infeasibility = np.max(residuals[1:3])
print(f"max infeasibility: {max_infeasibility}")
# compare to analytical solution
exact_solution = np.array([-1, 0])
sol_err = max(np.abs(solution - exact_solution))
# checks
if sol_err > TOL * 1e1:
raise Exception(
f"error of numerical solution wrt exact solution = {sol_err} > tol = {TOL*1e1}"
)
else:
print(f"matched analytical solution with tolerance {TOL}")
try:
if globalization == 'FIXED_STEP':
# import pdb; pdb.set_trace()
if max_infeasibility < 5.0:
raise Exception(
f"Expected max_infeasibility > 5.0 when using full step SQP on Marathos problem"
)
if iter != 10:
raise Exception(
f"Expected 10 SQP iterations when using full step SQP on Marathos problem, got {iter}"
)
if any(alphas[:iter] != 1.0):
raise Exception(
f"Expected all alphas = 1.0 when using full step SQP on Marathos problem"
)
elif globalization == 'MERIT_BACKTRACKING':
if max_infeasibility > 0.5:
raise Exception(
f"Expected max_infeasibility < 0.5 when using globalized SQP on Marathos problem"
)
if globalization_use_SOC == 0:
if FOR_LOOPING and iter != 57:
raise Exception(
f"Expected 57 SQP iterations when using globalized SQP without SOC on Marathos problem, got {iter}"
)
elif line_search_use_sufficient_descent == 1:
if iter not in range(29, 37):
# NOTE: got 29 locally and 36 on Github actions.
# On Github actions the inequality constraint was numerically violated in the beginning.
# This leads to very different behavior, since the merit gradient is so different.
# Github actions: merit_grad = -1.669330e+00, merit_grad_cost = -1.737950e-01, merit_grad_dyn = 0.000000e+00, merit_grad_ineq = -1.495535e+00
# Jonathan Laptop: merit_grad = -1.737950e-01, merit_grad_cost = -1.737950e-01, merit_grad_dyn = 0.000000e+00, merit_grad_ineq = 0.000000e+00
raise Exception(
f"Expected SQP iterations in range(29, 37) when using globalized SQP with SOC on Marathos problem, got {iter}"
)
else:
if iter != 12:
raise Exception(
f"Expected 12 SQP iterations when using globalized SQP with SOC on Marathos problem, got {iter}"
)
except Exception as inst:
if FOR_LOOPING and globalization == "MERIT_BACKTRACKING":
print(
"\nAcados globalized OCP solver behaves different when for looping due to different merit function weights.",
"Following exception is not raised\n")
print(inst, "\n")
else:
raise (inst)
if PLOT:
plt.figure()
axs = plt.plot(solution[0], solution[1], 'x', label='solution')
if FOR_LOOPING: # call solver in for loop to get all iterates
cm = plt.cm.get_cmap('RdYlBu')
axs = plt.scatter(iterates[:iter + 1, 0],
iterates[:iter + 1, 1],
c=range(iter + 1),
s=35,
cmap=cm,
label='iterates')
plt.colorbar(axs)
ts = np.linspace(0, 2 * np.pi, 100)
plt.plot(1 * np.cos(ts) + 0, 1 * np.sin(ts) - 0, 'r')
plt.axis('square')
plt.legend()
plt.title(
f"Marathos problem with N = {N}, x formulation, SOC {globalization_use_SOC}"
)
plt.show()
print(f"\n\n----------------------\n")
def solve_armijo_problem_with_setting(setting):
globalization = setting['globalization']
line_search_use_sufficient_descent = setting[
'line_search_use_sufficient_descent']
globalization_use_SOC = setting['globalization_use_SOC']
# create ocp object to formulate the OCP
ocp = AcadosOcp()
# set model
model = AcadosModel()
x = SX.sym('x')
# dynamics: identity
model.disc_dyn_expr = x
model.x = x
model.u = SX.sym('u', 0, 0) # [] / None doesnt work
model.p = []
model.name = f'armijo_problem'
ocp.model = model
# discretization
Tf = 1
N = 1
ocp.dims.N = N
ocp.solver_options.tf = Tf
# cost
ocp.cost.cost_type_e = 'EXTERNAL'
ocp.model.cost_expr_ext_cost_e = x @ x
ocp.model.cost_expr_ext_cost_custom_hess_e = 1.0 # 2.0 is the actual hessian
# constarints
ocp.constraints.idxbx = np.array([0])
ocp.constraints.lbx = np.array([-10.0])
ocp.constraints.ubx = np.array([10.0])
# options
ocp.solver_options.qp_solver = 'FULL_CONDENSING_QPOASES' # 'PARTIAL_CONDENSING_HPIPM' # FULL_CONDENSING_QPOASES
ocp.solver_options.hessian_approx = 'EXACT'
ocp.solver_options.integrator_type = 'DISCRETE'
ocp.solver_options.print_level = 0
ocp.solver_options.tol = TOL
ocp.solver_options.nlp_solver_type = 'SQP' # SQP_RTI, SQP
ocp.solver_options.globalization = globalization
ocp.solver_options.alpha_reduction = 0.9
ocp.solver_options.line_search_use_sufficient_descent = line_search_use_sufficient_descent
ocp.solver_options.globalization_use_SOC = globalization_use_SOC
ocp.solver_options.eps_sufficient_descent = 5e-1
SQP_max_iter = 200
ocp.solver_options.qp_solver_iter_max = 400
ocp.solver_options.nlp_solver_max_iter = SQP_max_iter
# create solver
ocp_solver = AcadosOcpSolver(ocp, json_file=f'{model.name}.json')
# initialize solver
xinit = np.array([1.0])
[ocp_solver.set(i, "x", xinit) for i in range(N + 1)]
# get stats
status = ocp_solver.solve()
ocp_solver.print_statistics()
iter = ocp_solver.get_stats('sqp_iter')[0]
alphas = ocp_solver.get_stats('alpha')[1:]
qp_iters = ocp_solver.get_stats('qp_iter')
print(f"acados ocp solver returned status {status}")
# get solution
solution = ocp_solver.get(0, "x")
print(f"found solution {solution}")
# print summary
print(f"solved Armijo test problem with settings {setting}")
print(
f"cost function value = {ocp_solver.get_cost()} after {iter} SQP iterations"
)
print(f"alphas: {alphas[:iter]}")
print(f"total number of QP iterations: {sum(qp_iters[:iter])}")
# compare to analytical solution
exact_solution = np.array([0.0])
sol_err = max(np.abs(solution - exact_solution))
print(f"error wrt analytical solution {sol_err}")
# checks
if ocp.model.cost_expr_ext_cost_custom_hess_e == 1.0:
if globalization == 'MERIT_BACKTRACKING':
if sol_err > TOL * 1e1:
raise Exception(
f"error of numerical solution wrt exact solution = {sol_err} > tol = {TOL*1e1}"
)
else:
print(f"matched analytical solution with tolerance {TOL}")
if status != 0:
raise Exception(
f"acados solver returned status {status} != 0.")
if line_search_use_sufficient_descent == 1:
if iter > 22:
raise Exception(f"acados ocp solver took {iter} iterations." + \
"Expected <= 22 iterations for globalized SQP method with aggressive eps_sufficient_descent condition on Armijo test problem.")
else:
if iter < 64:
raise Exception(f"acados ocp solver took {iter} iterations." + \
"Expected > 64 iterations for globalized SQP method without sufficient descent condition on Armijo test problem.")
elif globalization == 'FIXED_STEP':
if status != 2:
raise Exception(
f"acados solver returned status {status} != 2. Expected maximum iterations for full-step SQP on Armijo test problem."
)
else:
print(
f"Sucess: Expected maximum iterations for full-step SQP on Armijo test problem."
)
print(f"\n\n----------------------\n")
def export_pend_ode_model():
model_name = 'pend_ode'
# set up states & controls
q1 = SX.sym('q1')
q2 = SX.sym('q2')
dq1 = SX.sym('dq1')
dq2 = SX.sym('dq2')
b1 = SX.sym('b1')
b2 = SX.sym('b2')
x = vertcat(q1, q2, dq1, dq2, b1, b2)
# controls
tau1 = SX.sym('tau1')
tau = vertcat(tau1, 0)
u = vertcat(tau1)
# xdot
q1_dot = SX.sym('q1_dot')
q2_dot = SX.sym('q2_dot')
dq1_dot = SX.sym('dq1_dot')
dq2_dot = SX.sym('dq2_dot')
b1_dot = SX.sym('b1_dot')
b2_dot = SX.sym('b2_dot')
xdot = vertcat(q1_dot, q2_dot, dq1_dot, dq2_dot, b1_dot, b2_dot)
# algebraic variables
# z = None
# parameters
# set up parameters
m1 = SX.sym('m1') # mass link 1
m2 = SX.sym('m2') # mass link 2
l1 = SX.sym('l1') # length link 1
l2 = SX.sym('l2') # length link 2
lc1 = SX.sym('lc1') # cm link 1
lc2 = SX.sym('lc2') # cm link 2
g = SX.sym('g')
I1 = (1 / 3) * m1 * l1 * l1
I2 = (1 / 3) * m2 * l2 * l2
p = vertcat(m1, m2, l1, l2, lc1, lc2, g)
# system dynamics.
theta1 = m1 * lc1 * lc1 + m2 * l1 * l1 + I1
theta2 = m2 * lc2 * lc2 + I2
theta3 = m2 * l1 * lc2
theta4 = m1 * lc1 + m2 * l1
theta5 = m2 * lc2
# In matrix form, D(q) -> Inertia Matrix, C(dq,q) ->Coriolis Matrix, F(dq) -> Dissipation matrix
# D(q) ddq + C(q,dq) dq + F(dq) + g(q) = tau
D = SX.zeros(2, 2)
C = SX.zeros(2, 2)
F = SX.zeros(2, 1)
gVec = SX.zeros(2, 1)
D[0, 0] = theta1 + theta2 + 2 * theta3 * cos(q2)
D[1, 0] = theta2 + theta3 * cos(q2)
D[0, 1] = D[1, 0]
D[1, 1] = theta2
C[0, 0] = -theta3 * sin(q2) * dq2
C[1, 0] = -theta3 * sin(q2) * dq2 - theta3 * sin(q2) * dq1
C[0, 1] = theta3 * sin(q2) * dq1
C[1, 1] = 0
F[0, 0] = b1 * dq1
F[1, 0] = b2 * dq2
gVec[0, 0] = theta4 * g * cos(q1) + theta5 * g * cos(q1 + q2)
gVec[1, 0] = theta5 * g * cos(q1 + q2)
# dynamics
f_expl = vertcat(
dq1, dq2, mtimes(inv(D),
tau - mtimes(C, vertcat(dq1, dq2)) - F - gVec), 0, 0)
f_impl = xdot - f_expl
model = AcadosModel()
model.f_impl_expr = f_impl
model.f_expl_expr = f_expl
model.x = x
model.xdot = xdot
model.u = u
# model.z = []
model.p = p
model.name = model_name
return model
def generate(self, dae, name='tunempc', opts={}):
""" Create embeddable NLP solver
"""
from acados_template import AcadosModel, AcadosOcp, AcadosOcpSolver, AcadosSimSolver
# extract dimensions
nx = self.__nx
nu = self.__nu + self.__ns # treat slacks as pseudo-controls
# extract reference
ref = self.__ref
xref = np.squeeze(self.__ref[0][:nx], axis=1)
uref = np.squeeze(self.__ref[0][nx:nx + nu], axis=1)
# create acados model
model = AcadosModel()
model.x = ca.MX.sym('x', nx)
model.u = ca.MX.sym('u', nu)
model.p = []
model.name = name
# detect input type
n_in = dae.n_in()
if n_in == 2:
# xdot = f(x, u)
if 'integrator_type' in opts:
if opts['integrator_type'] == 'IRK':
xdot = ca.MX.sym('xdot', nx)
model.xdot = xdot
model.f_impl_expr = xdot - dae(model.x,
model.u[:self.__nu])
model.f_expl_expr = xdot
elif opts['integrator_type'] == 'ERK':
model.f_expl_expr = dae(model.x, model.u[:self.__nu])
else:
raise ValueError('Provide numerical integrator type!')
else:
xdot = ca.MX.sym('xdot', nx)
model.xdot = xdot
model.f_expl_expr = xdot
if n_in == 3:
# f(xdot, x, u) = 0
model.f_impl_expr = dae(xdot, model.x, model.u[:self.__nu])
elif n_in == 4:
# f(xdot, x, u, z) = 0
nz = dae.size1_in(3)
z = ca.MX.sym('z', nz)
model.z = z
model.f_impl_expr = dae(xdot, model.x, model.u[:self.__nu], z)
else:
raise ValueError(
'Invalid number of inputs for system dynamics function.')
if self.__gnl is not None:
model.con_h_expr = self.__gnl(model.x, model.u[:self.__nu],
model.u[self.__nu:])
if self.__type == 'economic':
model.cost_expr_ext_cost = self.__cost(model.x,
model.u[:self.__nu])
# create acados ocp
ocp = AcadosOcp()
ocp.model = model
ny = nx + nu
ny_e = nx
# set horizon length
ocp.dims.N = self.__N
# set cost module
if self.__type == 'economic':
# set cost function type to external (provided in model)
ocp.cost.cost_type = 'EXTERNAL'
else:
# set weighting matrices
if self.__type == 'tracking':
ocp.cost.W = self.__Href[0][0]
# set-up linear least squares cost
ocp.cost.cost_type = 'LINEAR_LS'
ocp.cost.W_e = np.zeros((nx, nx))
ocp.cost.Vx = np.zeros((ny, nx))
ocp.cost.Vx[:nx, :nx] = np.eye(nx)
Vu = np.zeros((ny, nu))
Vu[nx:, :] = np.eye(nu)
ocp.cost.Vu = Vu
ocp.cost.Vx_e = np.eye(nx)
ocp.cost.yref = np.squeeze(
ca.vertcat(xref,uref).full() - \
ct.mtimes(np.linalg.inv(ocp.cost.W),self.__qref[0][0].T).full(), # gradient term
axis = 1
)
ocp.cost.yref_e = np.zeros((ny_e, ))
if n_in == 4: # DAE flag
ocp.cost.Vz = np.zeros((ny, nz))
# initial condition
ocp.constraints.x0 = xref
# set inequality constraints
ocp.constraints.constr_type = 'BGH'
if self.__S['C'] is not None:
C = self.__S['C'][0][:, :nx]
D = self.__S['C'][0][:, nx:]
lg = -self.__S['e'][0] + ct.mtimes(C, xref).full() + ct.mtimes(
D, uref).full()
ug = 1e8 - self.__S['e'][0] + ct.mtimes(
C, xref).full() + ct.mtimes(D, uref).full()
ocp.constraints.lg = np.squeeze(lg, axis=1)
ocp.constraints.ug = np.squeeze(ug, axis=1)
ocp.constraints.C = C
ocp.constraints.D = D
if 'usc' in self.__vars:
if 'us' in self.__vars:
arg = [
self.__vars['x'], self.__vars['u'], self.__vars['us'],
self.__vars['usc']
]
else:
arg = [
self.__vars['x'], self.__vars['u'], self.__vars['usc']
]
Jsg = ca.Function(
'Jsg', [self.__vars['usc']],
[ca.jacobian(self.__h(*arg), self.__vars['usc'])])(0.0)
self.__Jsg = Jsg.full()[:-self.__nsc, :]
ocp.constraints.Jsg = self.__Jsg
ocp.cost.Zl = np.zeros((self.__nsc, ))
ocp.cost.Zu = np.zeros((self.__nsc, ))
ocp.cost.zl = np.squeeze(self.__scost.full(), axis=1)
ocp.cost.zu = np.squeeze(self.__scost.full(), axis=1)
# set nonlinear equality constraints
if self.__gnl is not None:
ocp.constraints.lh = np.zeros(self.__ns, )
ocp.constraints.uh = np.zeros(self.__ns, )
# terminal constraint:
x_term = self.__p_operator(model.x)
Jbx = ca.Function('Jbx', [model.x],
[ca.jacobian(x_term, model.x)])(0.0)
ocp.constraints.Jbx_e = Jbx.full()
ocp.constraints.lbx_e = np.squeeze(self.__p_operator(xref).full(),
axis=1)
ocp.constraints.ubx_e = np.squeeze(self.__p_operator(xref).full(),
axis=1)
for option in list(opts.keys()):
setattr(ocp.solver_options, option, opts[option])
self.__acados_ocp_solver = AcadosOcpSolver(ocp,
json_file='acados_ocp_' +
model.name + '.json')
self.__acados_integrator = AcadosSimSolver(ocp,
json_file='acados_ocp_' +
model.name + '.json')
# set initial guess
self.__set_acados_initial_guess()
return self.__acados_ocp_solver, self.__acados_integrator