反推
控制理论(社会学)
自适应控制
机器人
稳健性(进化)
Lyapunov稳定性
计算机科学
人工神经网络
李雅普诺夫函数
执行机构
有效载荷(计算)
控制工程
工程类
人工智能
控制(管理)
非线性系统
计算机网络
生物化学
化学
物理
量子力学
网络数据包
基因
作者
Sung Jin Yoo,Jin Bae Park,Yoon Ho Choi
出处
期刊:IEEE transactions on systems, man, and cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2006-12-01
卷期号:36 (6): 1342-1355
被引量:150
标识
DOI:10.1109/tsmcb.2006.875869
摘要
A new method for the robust control of flexible-joint (FJ) robots with model uncertainties in both robot dynamics and actuator dynamics is proposed. The proposed control system is a combination of the adaptive dynamic surface control (DSC) technique and the self-recurrent wavelet neural network (SRWNN). The adaptive DSC technique provides the ability to overcome the "explosion of complexity" problem in backstepping controllers. The SRWNNs are used to observe the arbitrary model uncertainties of FJ robots, and all their weights are trained online. From the Lyapunov stability analysis, their adaptation laws are induced, and the uniformly ultimately boundedness of all signals in a closed-loop adaptive system is proved. Finally, simulation results for a three-link FJ robot are utilized to validate the good position tracking performance and robustness against payload uncertainties and external disturbances of the proposed control system.
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