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Adaptive neural network tracking control of robot manipulators with prescribed performance

Xie, X-L; Hou, Z-G; Cheng, L; Ji, C; Tan, M; Yu, H


X-L Xie

Z-G Hou

L Cheng

C Ji

M Tan


In this paper, a controller for robot manipulators is proposed to guarantee the tracking error of the systems bounded by predefined decreasing boundary. In this control scheme, a multi-layer neural network is used to approximate the unknown non-linear items, and the robustifying control term is used to compensate the approximation errors. The adaptive laws of weights of the neural network and robustifying control term are derived based on the Lyapunov stability analysis, so that, under appropriate assumptions, the transient and steady-state error bounds can be guaranteed. Compared with the existing work, the adaptable parameters in the proposed method do not need an off-line training procedure for better approximation. Simulations performed on a two-link robot manipulator illustrate the developed controller and demonstrate its performance.

Journal Article Type Article
Online Publication Date Jul 29, 2011
Publication Date 2011-09
Deposit Date Jun 18, 2022
Journal Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering
Print ISSN 0959-6518
Electronic ISSN 2041-3041
Publisher SAGE Publications
Peer Reviewed Peer Reviewed
Volume 225
Issue 6
Pages 790-797
Keywords neural network, error transformation, prescribed performance
Public URL