Dynamic Model Updates and Prediction Horizon Optimizations in Self-Tuning Model Predictive Control

Model Predictive Control (MPC) is increasingly being adopted across various industrial sectors due to its capability to manage constraints and nonlinearities effectively. This paper presents a novel approach for dynamically updating the MPC model and optimizing the prediction horizon during operation, ensuring both real-time performance and robustness. The method seamlessly integrates hardware and software, allowing for continuous operation without any downtime. Ex- perimental results indicate that the proposed approach sustains optimal control performance, even in the face of changes in system characteristics caused by aging and wear.