Generalized Predictive Control (GPC) Algorithm in Intelligent Predictive Control Systems

In implementing the Generalized Predictive Control (GPC) algorithm using MATLAB, we can select various functions such as step functions and sine functions as simulation examples. This approach facilitates better understanding and application of the GPC algorithm while improving control system performance and accuracy. The implementation typically involves defining system models using transfer functions or state-space representations, designing prediction horizons and control horizons, and implementing the recursive optimization process using MATLAB's control system toolbox functions. Key implementation aspects include handling system constraints, tuning weighting matrices for output tracking and control effort, and utilizing MATLAB's quadratic programming solvers for optimal control sequence calculation. The simulation setup allows for analyzing system responses to different reference signals and disturbance scenarios, demonstrating the algorithm's robustness and predictive capabilities in real-time control applications.