Reinforcement learning based parameter optimization of active disturbance rejection control for autonomous underwater vehicle  

作　　者：SONG Wanping CHEN Zengqiang SUN Mingwei SUN Qinglin 

机构地区：[1]College of Artificial Intelligence,Nankai University,Tianjin 300350,China [2]Key Laboratory of Intelligent Robotics of Tianjin,Nankai University,Tianjin 300350,China

出　　处：《Journal of Systems Engineering and Electronics》2022年第1期170-179,共10页系统工程与电子技术（英文版）

基　　金：supported by the National Natural Science Foundation of China (61973175;61973172);Tianjin Natural Science Foundation (19JCZDJC32800)。

摘　　要：This paper proposes a liner active disturbance rejection control(LADRC) method based on the Q-Learning algorithm of reinforcement learning(RL) to control the six-degree-of-freedom motion of an autonomous underwater vehicle(AUV).The number of controllers is increased to realize AUV motion decoupling.At the same time, in order to avoid the oversize of the algorithm, combined with the controlled content, a simplified Q-learning algorithm is constructed to realize the parameter adaptation of the LADRC controller.Finally, through the simulation experiment of the controller with fixed parameters and the controller based on the Q-learning algorithm, the rationality of the simplified algorithm, the effectiveness of parameter adaptation, and the unique advantages of the LADRC controller are verified.

关 键 词：autonomous underwater vehicle(AUV) reinforcement learning(RL) Q-LEARNING linear active disturbance rejection control(LADRC) motion decoupling parameter optimization 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP273[自动化与计算机技术—控制科学与工程]