南京航空航天大学学报（英文版）2021，Vol.38Issue(2)：225-236,12.
基于数据的多源干扰SGCMG框架伺服系统鲁棒控制反馈再学习算法
Data-Based Feedback Relearning Algorithm for Robust Control of SGCMG Gimbal Servo System with Multi-source Disturbance
摘要
Abstract
Single gimbal control moment gyroscope(SGCMG)with high precision and fast response is an important attitude control system for high precision docking,rapid maneuvering navigation and guidance system in the aerospace field. In this paper,considering the influence of multi-source disturbance,a data-based feedback relearning (FR) algorithm is designed for the robust control of SGCMG gimbal servo system. Based on adaptive dynamic programming and least-square principle,the FR algorithm is used to obtain the servo control strategy by collecting the online operation data of SGCMG system. This is a model-free learning strategy in which no prior knowledge of the SGCMG model is required. Then,combining the reinforcement learning mechanism,the servo control strategy is interacted with system dynamic of SGCMG. The adaptive evaluation and improvement of servo control strategy against the multi-source disturbance are realized. Meanwhile,a data redistribution method based on experience replay is designed to reduce data correlation to improve algorithm stability and data utilization efficiency. Finally,by comparing with other methods on the simulation model of SGCMG,the effectiveness of the proposed servo control strategy is verified.关键词
控制力矩陀螺/反馈再学习算法/伺服控制/强化学习/多源干扰/自适应动态规划Key words
control moment gyroscope/feedback relearning algorithm/servo control/reinforcement learning/multi-source disturbance/adaptive dynamic programming分类
信息技术与安全科学引用本文复制引用
张勇,穆朝絮,鲁明..基于数据的多源干扰SGCMG框架伺服系统鲁棒控制反馈再学习算法[J].南京航空航天大学学报（英文版）,2021,38(2):225-236,12.基金项目
This work was supported by the Na-tional Natural Science Foundation of China(No.62022061),Tianjin Natural Science Foundation(No.20JCYBJC00880),and Beijing Key Laboratory Open Fund of Long-Life Tech-nology of Precise Rotation and Transmission Mechanisms. （No.62022061）
