Trajectory Tracking Control for Under-Actuated Hovercraft Using Differential Flatness and Reinforcement Learning-Based Active Disturbance Rejection Control  

作　　者：KONG Xiangyu XIA Yuanqing HU Rui LIN Min SUN Zhongqi DAI Li 

机构地区：[1]School of Automation,Beijing Institute of Technology,Beijing 100190,China

出　　处：《Journal of Systems Science & Complexity》2022年第2期502-521,共20页系统科学与复杂性学报（英文版）

基　　金：This paper was supported by the National Natural Science Foundation of China under Grant No.61720106010.

摘　　要：This paper proposes a scheme of trajectory tracking control for the hovercraft.Since the model of the hovercraft is under-actuated,nonlinear,and strongly coupled,it is a great challenge for the controller design.To solve this problem,the control scheme is divided into two parts.Firstly,we employ differential flatness method to find a set of flat outputs and consider part of the nonlinear terms as uncertainties.Consequently,we convert the under-actuated system into a full-actuated one.Secondly,a reinforcement learning-based active disturbance rejection controller(RL-ADRC)is designed.In this method,an extended state observer(ESO)is designed to estimate the uncertainties of the system,and an actorcritic-based reinforcement learning(RL)algorithm is used to approximate the optimal control strategy.Based on the output of the ESO,the RL-ADRC compensates for the total uncertainties in real-time,and simultaneously,generates the optimal control strategy by RL algorithm.Simulation results show that,compared with the traditional ADRC method,RL-ADRC does not need to manually tune the controller parameters,and the control strategy is more robust.

关 键 词：Active disturbance rejection control differential flatness reinforcement learning trajectory tracking control under-actuated system 

分 类 号：U664.82[交通运输工程—船舶及航道工程] TP273[交通运输工程—船舶与海洋工程]