Implementation of Model Predictive Control Using MATLAB Code

This article discusses Model Predictive Control (MPC), a significant optimization-based control method. MPC employs system modeling and prediction to optimize input signals for achieving optimal control performance. The implementation typically involves creating a mathematical model that characterizes system behavior, which is then used to predict future system states. Key implementation aspects include: designing prediction horizons, formulating cost functions, and solving optimization problems at each control interval. Through continuous model updating and re-optimization using algorithms like quadratic programming, MPC improves system control effectiveness. The core MATLAB implementation often involves functions such as mpcsys for system definition, mpccontroller for controller setup, and mpcmove for executing control actions. This approach finds extensive applications in modern control theory and engineering practices due to its ability to handle constraints and multivariable systems effectively.