Adaptive RBFNN control of robot manipulators with finite-time convergence

Chenguang Yang,Runxian Yang Runxian Yang,Fei Chen Fei Chen,Jing Na Jing Na

doi:10.1109/iecon.2016.7792980

Adaptive RBFNN control of robot manipulators with finite-time convergence

Chenguang Yang, Runxian Yang Runxian Yang + Show 2 more

Open Access

https://doi.org/10.1109/iecon.2016.7792980

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Publication Date: Oct 1, 2016
Citations: 14	License type: other-oa

Affiliation: Swansea University, Italian Institute of Technology, University of Bristol

#Radial Basis Function Neural Network #Finite-time Convergence + Show 8 more

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Abstract

In this paper, the position tracking control with finite-time convergence has been studied for a class of nonliear uncertain robot manipulators. Radial basis function neural network (RBFNN) based adaptive control is designed to compensate for the effect of the unknown dynamics. To achieve the finite-time convergence of both trajectory tracking error and RBFNN learning error, barrier Lyapunov functions (BLFs) and and filtering techniques are employed to design a performance function and a tracking error region to ensure position tracking error converge to a pair of specified bounds in a finite time. The effectiveness and efficiency of the proposed control method is tested and verified by simulation studies.

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