基于Q学习的高超声速飞行器自抗扰控制研究  

Research on Active Disturbance Rejection Control of Hypersonic Vehicle Based on Q-learning

作　　者：高强[1] 李旭[1] 吉月辉[1] 刘俊杰[1] GAO Qiang;LI Xu;JI Yuehui;LIU Junjie(School of Electrical Engineering and Automation,Tianjin University of Technology,Tianjin 300384,China)

机构地区：[1]天津理工大学电气工程与自动化学院,天津300384

出　　处：《控制工程》2024年第4期577-582,共6页Control Engineering of China

基　　金：国家自然科学基金资助项目(61975151,61308120)。

摘　　要：为实现高超声速飞行器姿态自抗扰控制的参数整定,提出一种模糊Q学习算法。首先,采用强化学习中的Q学习算法来实现姿态自抗扰控制参数的离线闭环快速自适应整定;然后,根据模糊控制的思路,将控制参数划分为不同区域,通过设定奖励,不断更新Q表;最后,将训练好的Q表用于飞行器的控制。仿真结果表明,相对于传统的线性自抗扰控制(linear active disturbance rejection control,LADRC)和滑模控制,基于Q学习的LADRC省去了人工调试参数的繁琐过程,且仍具有良好的跟踪效果。蒙特卡罗仿真测试结果验证了基于Q学习的LADRC的鲁棒性。A fuzzy Q-learning algorithm is proposed to adjust the parameters of attitude active disturbance rejection control for hypersonic vehicles.Firstly,the Q-learning algorithm in reinforcement learning is used to realize the fast off-line closed-loop adaptive tuning of attitude active disturbance rejection control parameters.Then,according to the idea of fuzzy control,the control parameters are divided into different areas,and the Q-table is constantly updated by setting rewards.Finally,the trained Q-table is used to control the aircraft.Compared with conventional linear active disturbance rejection control(LADRC)and sliding mode control,simulation results show that LADRC based on Q-learning saves the tedious process of manual parameter tuning,and still has good tracking performance.Monte Carlo simulation results verify the robustness of LADRC based on Q-learning.

关 键 词：高超声速飞行器 姿态控制 自抗扰控制 Q学习 参数整定 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]