Model Predictive Control Implementation Using MATLAB

In this document, we explore how to implement Model Predictive Control (MPC) using MATLAB. MPC is a widely-used control methodology that delivers superior control performance across industrial and manufacturing applications. This advanced control strategy optimizes control objectives by computing sequences of control variables through iterative optimization algorithms. As a nonlinear control technique, MPC requires constructing mathematical models to represent system dynamics, which can then be simulated and controlled using MATLAB's robust toolboxes. Key implementation involves using MATLAB's Model Predictive Control Toolbox functions like mpc for controller object creation, sim for closed-loop simulation, and optimization solvers for constraint handling. This guide covers MPC fundamentals, practical MATLAB implementation techniques including state-space model formulation, prediction horizon configuration, and constraint definition using mpc properties. We'll demonstrate simulation methodologies through mpcmove commands and analysis procedures using plotting functions to evaluate control performance metrics. This knowledge will enable deeper understanding of MPC operational principles and enhance practical application effectiveness through proper MATLAB coding practices and algorithm tuning.