Global adaptive tracking control of robot manipulators using neural networks with finite-time learning convergence
Global adaptive tracking control of robot manipulators using neural networks with finite-time learning convergence
复制标题
使用具有有限时间学习收敛的神经网络的机器人操纵器的全局自适应跟踪控制
DOI：
10.1007/s12555-016-0515-7
复制
发表时间：
2017-07
影响因子：
3.2
通讯作者：
Su Chun-Yi
中科院分区：
文献类型：
--
作者：
Yang Chenguang;Teng Tao;Xu Bin;Li Zhijun;Na Jing;Su Chun-Yi
In this paper, the global adaptive neural control with finite-time (FT) convergence learning performance for a general class of nonlinear robot manipulators has been investigated. The scheme proposed in this paper offers a subtle blend of neural controller with robust controller, which palliates the limitation of neural approximation region to ensure globally uniformly ultimately bounded (GUUB) stability by integrating a switching mechanism. Morever, the proposed scheme guarantees the estimated neural weights converging to optimal values in finite time by embedding an adaptive learning algorithm driven by the estimated weights error. The optimal weights obtained through the learning process of the neural networks (NNs) will be reused next time for repeated tasks, and can thus reduce computational load, improve transient performance and enhance robustness. The simulation studies have been carried out to demonstrate the superior performance of the controller in comparison to the conventional methods.
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DOI：
10.1109/cdc.2003.1271916
发表时间：
2003-12
期刊：
42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475)
影响因子：
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发表时间：
2014-03
影响因子：
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DOI：
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发表时间：
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期刊：
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影响因子：
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DOI：
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发表时间：
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期刊：
International Journal of Control, Automation and Systems
影响因子：
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