摘要
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.
单框架控制力矩陀螺(Single gimbal control moment gyroscope,SGCMG)具有高精度、快速响应的特点,是航天领域高精度对接、快速机动导航和制导系统的重要姿态控制系统。本文考虑多源干扰的影响,设计了一种基于数据的反馈再学习(Feedback relearning,FR)算法,用于SGCMG框架伺服系统的鲁棒控制。基于自适应动态规划和最小二乘原理,通过采集SGCMG系统的在线运行数据,采用FR算法得到伺服控制策略。这是一种无模型学习策略,无须事先了解SGCMG模型。进而,基于强化学习机制将伺服控制策略与SGCMG系统动态相互作用,可以实现伺服控制策略对多源干扰的自适应评估和改进。同时,设计了一种基于经验回放的数据重分配方法,降低了数据相关性,提高了算法稳定性和数据利用率。最后,在SGCMG仿真模型上与其他方法进行了比较,验证了所提出的伺服控制策略的有效性。
基金
This work was supported by the National Natural Science Foundation of China(No.62022061)
Tianjin Natural Science Foundation(No.20JCYBJC00880)
Beijing Key Laboratory Open Fund of Long-Life Technology of Precise Rotation and Transmission Mechanisms.
