Implementing GPC Algorithm for Predictive Control Using MATLAB

In this article, we will explore how to implement the Generalized Predictive Control (GPC) algorithm for predictive control using MATLAB. We will first explain the fundamental principles of the GPC algorithm and its typical application scenarios. The implementation will cover key MATLAB functions such as system identification using arx or pem for model parameter estimation, cost function formulation with quadratic programming via quadprog, and receding horizon control implementation through iterative prediction loops. We will then detail how to apply this algorithm in practical control systems, including handling constraints using fmincon and tuning prediction/control horizons. Additionally, we will discuss performance optimization techniques such as weight matrix adjustment and real-time computation efficiency improvements. Practical tips and lessons learned—like handling non-minimum phase systems and numerical stability with chol decomposition—will be provided to help readers better understand and apply GPC. Finally, we will examine future developments of the algorithm, including multi-objective optimization and adaptive GPC, along with potential application domains and challenges like computational complexity and robustness assurance.