def make_custom_mpc_model(state_space_model, prediction_horizon,
control_horizon):
"""
Make the MPC model for the Euler state space model. This function exposes the interface
to the MPC class instance, so it is easy to see what is being passed as the arguments from the state
space model. Furthermore, the control and prediction horizon is also added.
:param state_space_model: Euler model state space model.
:param prediction_horizon: Length of the prediction horizon.
:param control_horizon: Length of the control horizon
:return: Augmented state space model dictionary with the state space model as a new entry.
"""
num_states = state_space_model["num_states"]
ref = set_custom_model_ref(prediction_horizon, num_states)
mpc_model = mpco.MpcObj(state_space_model["system_model"],
state_space_model["B_matrix"], control_horizon,
prediction_horizon, state_space_model["Q"],
state_space_model["R"], state_space_model["x0"],
state_space_model["u0"], ref,
state_space_model["b_disturbance"],
state_space_model["disturbance_array"])
state_space_model["mpc_model"] = mpc_model
return state_space_model
def make_euler_mpc_model(state_space_model, prediction_horizon,
control_horizon):
"""
Make the MPC model for the Euler state space model. This function exposes the interface
to the MPC class instance, so it is easy to see what is being passed as the arguments from the state
space model. Furthermore, the control and prediction horizon is also added.
:param state_space_model: Euler model state space model.
:param prediction_horizon: Length of the prediction horizon.
:param control_horizon: Length of the control horizon
:return: Augmented state space model dictionary with the state space model as a new entry.
"""
num_states = state_space_model["num_states"]
ref = set_euler_ref(prediction_horizon, num_states)
mpc_model = mpco.MpcObj(
state_space_model["system_model"],
state_space_model["b_matrix"],
control_horizon,
prediction_horizon,
state_space_model["Q"],
state_space_model["R"],
input_cost=state_space_model["S"],
initial_control_signal=state_space_model["u0"],
ref=ref,
operating_point=state_space_model["operating_point"],
input_matrix_d=state_space_model["b_disturbance"],
lower_bounds_input=state_space_model["lower_bounds_input"],
lower_bounds_slew_rate=state_space_model["lower_bounds_slew_rate"],
lower_bounds_states=state_space_model["lower_bounds_states"],
upper_bounds_input=state_space_model["upper_bounds_input"],
upper_bounds_slew_rate=state_space_model["upper_bounds_slew_rate"],
upper_bounds_states=state_space_model["upper_bounds_states"])
state_space_model["mpc_model"] = mpc_model
return state_space_model
import casadi as ca
from controller import mpc, mpco
import numpy as np
A = ca.DM([[1, 0], [0, 1.5]])
B = ca.DM([[0.1, 0], [0, 0.5]])
Hp = 40
Hu = 30
Q = ca.DM([[1, 0], [0, 3]])
Qb = mpc.blockdiag(Q, Hp)
R = ca.DM([[1, 0], [0, 2]])
Rb = mpc.blockdiag(R, Hu)
x0 = ca.DM([[10], [11]])
u_prev = ca.DM([-1, -3])
ref = ca.DM.ones(Hp * 2, 1)
ref[1::A.size1()] = np.cumsum(ref[1::A.size1()]) / 20 + 2
mmpc = mpco.MpcObj(A, B, Hu, Hp, Q, R, x0, u_prev, ref)
for i in range(0, 10):
input("press key")
lower_bounds_slew_rate = None
lower_bounds_states = None
upper_bounds_input = None
upper_bounds_slew_rate = None
upper_bounds_states = None
#lower_bounds_states = operating_point -100
mmpc = mpco.MpcObj(Ap,
Bp,
Hu,
Hp,
Q,
R,
ref=ref,
initial_control_signal=u0,
input_matrix_d=Bp_d,
lower_bounds_input=lower_bounds_input,
lower_bounds_slew_rate=lower_bounds_slew_rate,
upper_bounds_slew_rate=upper_bounds_slew_rate,
upper_bounds_input=upper_bounds_input,
lower_bounds_states=lower_bounds_states,
upper_bounds_states=upper_bounds_states,
operating_point=operating_point,
input_cost=S,
input_linear_cost=L)
mmpc.step(x0, u0, ref, initial_disturbance, dist)
mmpc.plot_progress({'drawU': 'U'})
mmpc.print_solver()
mmpc.print_result()
steps = 100