GPC Algorithm in Intelligent Predictive Control with MATLAB Implementation

This project implements the Generalized Predictive Control (GPC) algorithm using MATLAB programming language, with simulation examples using various function types including step functions and sine functions. Intelligent predictive control algorithms represent model-based control methods that predict future system responses and make control decisions based on these predictions, significantly enhancing system control performance. The GPC algorithm implementation involves several key MATLAB functions and programming approaches: - System identification and model parameter estimation using MATLAB's System Identification Toolbox - Cost function minimization for optimal control sequence calculation - Recursive prediction equations implementation using difference equations - Real-time control law computation with receding horizon strategy In practical applications, the GPC algorithm finds extensive use in various control problems such as mechanical systems control, power system regulation, and chemical process control. Through MATLAB implementation, we can efficiently code the GPC algorithm and validate its performance through comprehensive simulations. The simulation framework includes: - Designing different input signals (step, sinusoidal, random) to test algorithm robustness - Implementing performance metrics calculation (ISE, IAE, settling time) - Creating comparative analysis with traditional PID controllers - Developing real-time plotting functions for system response visualization For simulation purposes, we select different function types as input signals, such as step functions (testing transient response) and sine functions (evaluating frequency response characteristics), to simulate diverse control scenarios. This approach enables comprehensive understanding of GPC algorithm characteristics and applicability, facilitating more accurate control decisions in practical applications. The MATLAB code structure typically includes main simulation scripts, GPC calculation functions, system modeling modules, and result visualization components.