Predictive Control for Step Function Tracking

In this implementation, we employ predictive control methodology to track step function references. The system utilizes a specifically defined plant model and implements a control algorithm through MATLAB programming. The core implementation involves designing a cost function that minimizes the difference between predicted outputs and reference trajectories over a finite horizon. Key components include state-space modeling, constraint handling, and receding horizon implementation. The program successfully achieves precise tracking of step function commands while maintaining excellent control performance. This approach employs quadratic programming optimization at each time step to compute optimal control inputs, incorporating system constraints through efficient numerical methods. The algorithm demonstrates robustness against model uncertainties and disturbances. This methodology not only proves effective for the current step-function tracking problem but can be extended to various similar control tasks. Our research findings confirm that predictive control serves as a powerful framework that can significantly contribute to diverse practical applications, particularly in systems requiring anticipatory control actions and constraint management. The implementation showcases how model predictive control (MPC) can handle reference tracking problems while maintaining system stability and performance.