Hi 
I am using the python interface to generate c code. I am using the c code in my c++ project. I don’t know much about the difference between SQP and SQP RTI. However, from testing in python i found SQP RTI to be faster and more reliable. Therefore I want to use it in my c code.
The solver works fine in python, however the generated code is not working. There are no errors, however the solution violates the model. When I change to SQP it works. I have also tried different solvers. Is there something extra I need to do in the c code when using SQP RTI?
If you are intrested here is a example of when the generated code is not working as expected. I am modeling a car using the bicycle model and I want to follow the line y=0 with velocity = 4.
from acados_template import AcadosOcp, AcadosModel, AcadosOcpSolver
import scipy.linalg
from casadi import SX, vertcat, sin, cos
import numpy as np
def carBicycleModel():
modelName = "bicycle_model"
Lf = 2.65
x1 = SX.sym("x1") # x position
y1 = SX.sym("y1") # y position
psi = SX.sym("psi") # angle of car
v = SX.sym("v") # velocity
delta = SX.sym("delta") # steering angle
a = SX.sym("a") # acceleration
x = vertcat(x1, y1, psi, v)
u = vertcat(delta, a)
x1Dot = SX.sym("x1_dot")
y1Dot = SX.sym("y1_dot")
psiDot = SX.sym("psi_dot")
vDot = SX.sym("v_dot")
xDot = vertcat(x1Dot, y1Dot, psiDot, vDot)
fExpl = vertcat(
v*cos(psi),
v*sin(psi),
v/Lf*delta,
a)
fImpl = xDot - fExpl
model = AcadosModel()
model.name = modelName
model.f_expl_expr = fExpl
model.f_impl_expr = fImpl
model.xdot = xDot
model.x = x
model.u = u
model.p = []
return model
def main():
ocp = AcadosOcp()
ocp.model = carBicycleModel()
Tf = 2.0
N = 20
ocp.dims.N = N
nx = ocp.model.x.size()[0]
nu = ocp.model.u.size()[0]
ny = nx + nu
ocp.cost.cost_type = "LINEAR_LS"
ocp.cost.cost_type_e = "LINEAR_LS"
Q = 2*np.diag([0, 1e1, 1e2, 1e0])
R = 2*np.diag([1e-1, 1e-1])
ocp.cost.W = scipy.linalg.block_diag(Q, R)
ocp.cost.W_e = Q
Vx = np.zeros((ny, nx))
Vx[:nx, :nx] = np.eye(nx)
ocp.cost.Vx = Vx
ocp.cost.Vx_e = np.eye(nx)
Vu = np.zeros((ny, nu))
Vu[nx, 0] = 1
Vu[nx+1, 1] = 1
ocp.cost.Vu = Vu
ocp.cost.yref = np.array([0, 0, 0, 4, 0, 0])
ocp.cost.yref_e = np.array([0, 0, 0, 4])
aMax = 1
deltaMax = 0.57
ocp.constraints.constr_type = "BGH"
ocp.constraints.lbu = np.array([-deltaMax, -aMax])
ocp.constraints.ubu = np.array([deltaMax, aMax])
ocp.constraints.idxbu = np.array([0, 1])
ocp.constraints.x0 = np.array([0, 2, -np.pi/4, 0])
ocp.solver_options.qp_solver = "PARTIAL_CONDENSING_HPIPM"
ocp.solver_options.hessian_approx = "GAUSS_NEWTON"
ocp.solver_options.integrator_type = "ERK"
ocp.solver_options.nlp_solver_type = "SQP_RTI"
ocp.solver_options.qp_solver_cond_N = N
ocp.solver_options.tf = Tf
ocp_solver = AcadosOcpSolver(ocp, 'acados_ocp_' + ocp.model.name + '.json')
ocp_solver.solve()
xTraj = np.ndarray((N, nx))
for i in range(N):
xTraj[i, :] = ocp_solver.get(i, "x")
print("---xTraj---")
print(xTraj)
main()
Thanks for your help!