import numpy as np
from predictivecontrol import MPC

# Define your state-space matrices, i.e. A, B, C

# Instantiate controller
mpc = MPC(A,B,C)

# Set prediction and control horizons
mpc.set_predict_horizon(10)
mpc.set_control_horizon(4)

# Set control restrictions (currently variation and amplitude)
mpc.umin, mpc.umax = np.array([0]), np.array([100])     # Considering a SISO system
mpc.dumin, mpc.dumax = np.array([-0.5]), np.array([0.5])

# Set setpoints and output weights
mpc.set_reference(np.array([10]))
mpc.set_output_weights(np.array([1]))

mpc.run()

This package currently contains 4 examples:

• Voltage control in a simulated First-order RC Lowpass filter;
• Motor shaft angular velocity control in a simulated DC Motor;
• Angular velocity control in 3-DoF rotating rigid body (3-input-3-output MIMO);
• Toy mock MIMO systems, modelled from arbitrary transfer matrices;

You can run the examples with:

python <example>.py

If you want to plot the results, you must install the matplotlib package:

pip install matplotlib

The plotted results you will see are (for DC motor example):

• Test MPC with a different model [DONE]
• Extend MPC to support MIMO systems [DONE]
• Test MPC with MIMO systems [DONE]
• Test E-MPC SISO and MIMO
• Account for external disturbances in MPC SISO and MIMO
• Account for external disturbances in E-MPC SISO and MIMO

Code inspired by Prof. Dr. Aurelio Salton's MPC matlab example.

• Renan Maidana