Regular Papers

International Journal of Control, Automation and Systems 2017; 15(4): 1916-1924

Published online July 20, 2017

https://doi.org/10.1007/s12555-016-0515-7

© The International Journal of Control, Automation, and Systems

Global Adaptive Tracking Control of Robot Manipulators Using Neural Networks with Finite-time Learning Convergence

Chenguang Yang*, Tao Teng, Bin Xu, Zhijun Li, Jing Na and Chun-Yi Su

South China University of Technology

Abstract

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. Moreover, 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."

Keywords Finite-time learning convergence, globally uniformly ultimate boundedness, neural networks, robot manipulators.

Article

Regular Papers

International Journal of Control, Automation and Systems 2017; 15(4): 1916-1924

Published online August 1, 2017 https://doi.org/10.1007/s12555-016-0515-7

Copyright © The International Journal of Control, Automation, and Systems.

Global Adaptive Tracking Control of Robot Manipulators Using Neural Networks with Finite-time Learning Convergence

Chenguang Yang*, Tao Teng, Bin Xu, Zhijun Li, Jing Na and Chun-Yi Su

South China University of Technology

Abstract

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. Moreover, 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."

Keywords: Finite-time learning convergence, globally uniformly ultimate boundedness, neural networks, robot manipulators.

IJCAS
May 2024

Vol. 22, No. 5, pp. 1461~1759

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