引用本文:雷荣华,陈力.空间机器人基于比例因子识别的自校正反馈神经网络容错算法设计[J].控制与决策,2020,35(8):1833-1840
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空间机器人基于比例因子识别的自校正反馈神经网络容错算法设计
雷荣华,陈力
(福州大学机械工程及自动化学院,福州350116)
摘要:
针对执行器发生部分失效故障的空间机器人,提出一种基于比例因子识别的自校正反馈神经网络容错算法.首先,针对无故障系统设计一种常规的神经网络控制算法;然后,利用比例因子观测器对真实的比例因子进行识别;最后,将该神经网络控制算法与识别的比例因子相结合,得到一种具有容错功能的自校正反馈神经网络控制算法.控制器与观测器的稳定性判据均由Lyapunov函数法严格给出,并通过数值仿真表明所提出控制策略的可行性.
关键词:  空间机器人  执行器故障  比例因子  观测器  神经网络  容错控制
DOI:10.13195/j.kzyjc.2018.1481
分类号:TP241
基金项目:国家自然科学基金项目(11372073).
Scaling factor identification based self-tuned feedback neural network fault-tolerant algorithm design for space robot
LEI Rong-hua,CHEN Li
(School of Mechanical Engineering and Automation,Fuzhou University,Fuzhou350116,China)
Abstract:
A scaling factor identification based self-tuned feedback neural network fault-tolerant algorithm is proposed for space robot systems with partial loss of actuator effectiveness. Firstly, a conventional neural network control algorithm is designed for the fault-free system. Then, the real scaling factors are identified by using the scaling factor observer. Finally, a self-tuned feedback neural network fault-tolerant control algorithm is obtained by combining the above neural network control algorithm with the identified scaling factors. The stability criteria of observers and controllers are given strictly based on Lyapunov function method. Numerical simulation verifies the feasibility of the control method.
Key words:  space robot  actuator fault  scaling factor  observer  neural network  fault-tolerant control

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