Regular Papers

International Journal of Control, Automation and Systems 2019; 17(3): 783-792

Published online February 22, 2019

https://doi.org/10.1007/s12555-018-0210-y

© The International Journal of Control, Automation, and Systems

Robust Adaptive Sliding Mode Neural Networks Control for Industrial Robot Manipulators

Vu Thi Yen*, Wang Yao Nan, and Pham Van Cuong

Hunan University

Abstract

This paper proposes an original robust adaptive controller by using Radial Basis Function Neural networks (RBFNNs) for industrial robot manipulators (IRMs) in uncertain dynamical environments. This suggested control structure combines sliding mode technique, RBFNNs approximation and adaptive technique to improve the high accuracy of the tracking control. The proposed RBFNNs can deal the small problems successful because of its simple structure, faster training update laws and better approximation for the unknown dynamic of IRMs. All the parameters of the proposed control system are determined by Lyapunov stability theorem, and tuned online by an adaptive learning algorithm. Therefore, the stability, robustness and desired tracking performance of RBFNNs for IRMs are guaranteed. The simulations and experimental performed on a three-link IRMs are proposed in comparison with proportional integral differential (PID) and adaptive Fuzzy (AF) control to prove the robustness and efficiency of the RBFNNs."

Keywords Adaptive control, industrial robot, neural networks, RBF network.

Article

Regular Papers

International Journal of Control, Automation and Systems 2019; 17(3): 783-792

Published online March 1, 2019 https://doi.org/10.1007/s12555-018-0210-y

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

Robust Adaptive Sliding Mode Neural Networks Control for Industrial Robot Manipulators

Vu Thi Yen*, Wang Yao Nan, and Pham Van Cuong

Hunan University

Abstract

This paper proposes an original robust adaptive controller by using Radial Basis Function Neural networks (RBFNNs) for industrial robot manipulators (IRMs) in uncertain dynamical environments. This suggested control structure combines sliding mode technique, RBFNNs approximation and adaptive technique to improve the high accuracy of the tracking control. The proposed RBFNNs can deal the small problems successful because of its simple structure, faster training update laws and better approximation for the unknown dynamic of IRMs. All the parameters of the proposed control system are determined by Lyapunov stability theorem, and tuned online by an adaptive learning algorithm. Therefore, the stability, robustness and desired tracking performance of RBFNNs for IRMs are guaranteed. The simulations and experimental performed on a three-link IRMs are proposed in comparison with proportional integral differential (PID) and adaptive Fuzzy (AF) control to prove the robustness and efficiency of the RBFNNs."

Keywords: Adaptive control, industrial robot, neural networks, RBF network.

IJCAS
May 2024

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

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