MATLAB Implementation of Constrained Predictive Control

In control systems, constrained predictive control is a widely used method that achieves optimal control strategy solutions under input constraints by predicting system states and model behavior, thereby enabling perfect system control. This approach effectively prevents controller-induced system damage while maintaining system stability and controllability. The MATLAB implementation typically involves formulating a quadratic programming problem at each time step, where the cost function minimizes tracking errors and control efforts subject to constraints on input magnitudes and rates. Key functions like quadprog are employed to solve the optimization problem, while the control horizon and prediction window parameters are tuned based on system dynamics. Due to these advantages, constrained predictive control finds extensive applications in industrial automation, robotic control, chemical production, and other fields where safety constraints and optimal performance are critical.