典型文献
Reinforcement learning based parameter optimization of active disturbance rejection control for autonomous underwater vehicle
文献摘要：
This paper proposes a liner active disturbance rejec-tion control(LADRC)method based on the Q-Learning al-gorithm 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 de-coupling.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 adapt-ation of the LADRC controller.Finally,through the simulation ex-periment of the controller with fixed parameters and the control-ler based on the Q-learning algorithm,the rationality of the sim-plified algorithm,the effectiveness of parameter adaptation,and the unique advantages of the LADRC controller are verified.
文献关键词：
中图分类号：
作者姓名：
SONG Wanping;CHEN Zengqiang;SUN Mingwei;SUN Qinglin
作者机构：
College of Artificial Intelligence,Nankai University,Tianjin 300350,China;Key Laboratory of Intelligent Robotics of Tianjin,Nankai University,Tianjin 300350,China
文献出处：
引用格式：
[1]SONG Wanping;CHEN Zengqiang;SUN Mingwei;SUN Qinglin-.Reinforcement learning based parameter optimization of active disturbance rejection control for autonomous underwater vehicle)[J].系统工程与电子技术（英文版）,2022(01):170-179
A类：
oversize
B类：
Reinforcement,learning,optimization,active,disturbance,rejection,autonomous,underwater,vehicle,This,paper,proposes,liner,LADRC,method,Learning,reinforcement,RL,six,degree,freedom,motion,AUV,number,controllers,increased,realize,coupling,At,same,order,avoid,algorithm,combined,controlled,content,simplified,constructed,Finally,through,simulation,ex,periment,fixed,parameters,rationality,effectiveness,adaptation,unique,advantages,are,verified
AB值：
0.456064
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