Knowledge-Data-Driven Model Predictive Control for a Class of Nonlinear Systems

Abstract

Model predictive control (MPC) has been considered as a promising alternative for the control of nonlinear systems. However, this controller suffers from a challenge that it is difficult to deal with the complex nonlinear systems with incomplete datasets. To solve this problem, a novel MPC, by utilizing knowledge-data-driven model (KDDM), is designed and analyzed in this article. In comparison with the existing literatures, this knowledge-data-driven MPC (KDD-MPC) contains these following contributions. First, a systematic strategy is developed to reduce the online computational burden of KDD-MPC. Therefore, this KDD-MPC can own fast action to achieve favorable control performance. Second, the proposed KDDM intends to not only make full use of limited state information from the current model but also effectively leverage the knowledge from the reference model in the learning process. Therefore, it is more efficient for the complex nonlinear systems with insufficient data. Third, a novel transfer learning mechanism is designed to determine the optimal control sequence of KDD-MPC with strong adaptability. Therefore, it is suitable to achieve the desired control performance for engineering implementations. Finally, the benchmark problem and industrial application are provided to demonstrate the attractiveness and effectiveness of KDD-MPC.