Learning-enabled stochastic predictive control for nonlinear discrete-time step backward high-order fully actuated systems

作     者：Ning, Chao Zhao, Junhao Wang, Han 

作者机构：Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE》 (Int. J. Syst. Sci.)

年 卷 期：2025年第56卷第10期

页      面：2431-2446页

核心收录：

基　　金：National Natural Science Foundation of China [62103264  62473256] 

主　　题：High-order fully actuated model stochastic model predictive control principal component analysis kernel density estimation 

摘      要：In this paper, we seamlessly integrate machine learning techniques with stochastic Model Predictive Control (MPC) to address the regulation problem of nonlinear discrete-time step backward High-Order Fully Actuated (HOFA) systems with additive disturbance. By exploiting the full-actuation characteristic of the HOFA system, we neatly eliminate the non-linearity of the system, thus circumventing the complex computation of uncertainty propagation in nonlinear stochastic MPC. To cope with the random disturbance, its probability distribution on each principal component is well captured from data based on principal component analysis, and the uncertainty bound is effectively estimated via kernel density estimation and quantile functions. Based upon such probabilistic information, we impose constraint tightening on the state limits and define terminal sets by drawing on the concept of tubes. On this basis, we employ stochastic MPC for receding horizon control of HOFA systems, of which the recursive feasibility and stability are proved theoretically. Finally, numerical experiments and an application to hydrogen electrolyzer temperature control are used to demonstrate the merits of the proposed approach in comparison with state-of-the-art methods.