MATLAB Code Implementation for Prediction Functions

This article presents MATLAB-based implementations of predictive control functions. The programs analyze system responses to step inputs and evaluate control performance under various disturbance conditions. Through these implementations, users can achieve optimized control system performance with configurable parameters. The code architecture allows custom parameter adjustments to accommodate diverse application requirements. Detailed implementation methodologies include recursive prediction algorithms, disturbance rejection mechanisms, and real-time parameter adaptation logic. Practical application scenarios demonstrate the implementation of cost function optimization, constraint handling, and stability analysis within the predictive control framework. The provided examples illustrate key MATLAB functions including system identification routines, prediction horizon configuration, and performance validation metrics to facilitate comprehensive understanding and practical application.