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. Experimental results indicate that the proposed approach sustains optimal control performance, even in the face of changes in system characteristics caused by aging and wear.