Policy Learning for Nonlinear Model Predictive Control with Application to USVs

2022/11/18 by Rizhong Wang, Wang, Rizhong, Huiping Li +7 · 1 citation
Engineering · #Advanced Control Systems Optimization #FOS: Computer and information sciences #Fault Detection and Control Systems #Fuel Cells and Related Materials #Robotics (cs.RO)

paper · pdf · doi:10.48550/arxiv.2211.10075

openalex publication_date 2022/11/18 · openalex created_date 2022/11/28 · openalex updated_date 2026/07/28

Abstract

The unaffordable computation load of nonlinear model predictive control (NMPC) has prevented it for being used in robots with high sampling rates for decades. This paper is concerned with the policy learning problem for nonlinear MPC with system constraints, and its applications to unmanned surface vehicles (USVs), where the nonlinear MPC policy is learned offline and deployed online to resolve the computational complexity issue. A deep neural networks (DNN) based policy learning MPC (PL-MPC) method is proposed to avoid solving nonlinear optimal control problems online. The detailed policy learning method is developed and the PL-MPC algorithm is designed. The strategy to ensure the practical feasibility of policy implementation is proposed, and it is theoretically proved that the closed-loop system under the proposed method is asymptotically stable in probability. In addition, we apply the PL-MPC algorithm successfully to the motion control of USVs. It is shown that the proposed algorithm can be implemented at a sampling rate up to 5 Hz with high-precision motion control. The experiment video is available via:\urlhttps://v.youku.com/v_show/id_XNTkwMTM0NzM5Ng==.html

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• Active Disturbance Rejection Control for Trajectory Tracking of a Seagoing USV: Design, Simulation, and Field Experiments

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