Improved offset-free model predictive control utilizing learned model-plant mismatch map

The requirement for a framework that effectively overcomes the limitation of model-based and data-driven control strategies by combining both methods continues to grow. In this study, we propose an approach that learns the model-plant mismatch map and utilizes it based on the offset-free model predictive control (MPC). Specifically, the mismatch map is learned via general regression neural network (GRNN) that has been applied in broad range of fields based on the data from the process, and then the learned mismatch information is provided to the MPC system. In addition, since the approximated mismatch map via GRNN cannot be perfect, an additional supplementary disturbance estimator is utilize to ensure the zero-offset tracking property. Finally, the learned and supplementary disturbance signals are applied to the target problem and the optimal control problem based on the offset-free MPC framework. The effectiveness of the proposed combined model-based and data driven framework is demonstrated by closed-loop simulation. The result shows that the proposed framework can improve the closed-loop tracking performance by utilizing both the learned mismatch information from GRNN and the stabilizing property of the supplementary disturbance estimator. Copyright (C) 2022 The Authors.