MATLAB Implementation of Generalized Predictive Control

This MATLAB implementation of generalized predictive control utilizes advanced algorithms and techniques to achieve highly accurate tracking and prediction capabilities. The implementation typically employs key functions such as system identification using recursive least squares (RLS) methods, optimal predictor formulation based on Diophantine equations, and receding horizon optimization techniques. The code structure generally includes modules for parameter estimation, prediction calculation, and control law optimization using quadratic programming solvers. This implementation not only helps users better understand and analyze system dynamics but also provides robust support for future decision-making processes. Through proper tuning of prediction horizons and control weights, the algorithm demonstrates remarkable performance in various applications. Whether applied in industrial control systems, economic forecasting, or other domains, this MATLAB implementation of generalized predictive control offers significant potential, providing users with enhanced opportunities for success in complex predictive control scenarios.