|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
International Journal of Control, Automation, and Systems, vol. 15, no. 4, pp.1916-1924, 2017
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
"Finite-time learning convergence, globally uniformly ultimate boundedness, neural networks, robot manipulators."