Transactions of Nanjing University of Aeronautics & Astronautics
Data-Based Feedback Relearning Algorithm for Robust Control of SGCMG Gimbal Servo System with Multi-source Disturbance
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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.
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Clc Number:
TN925
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