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Adaptive Trajectory Neural Network Tracking Control for Industrial Robot Manipulators with Deadzone Robust Compensator

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  • Intelligent Control and Applications
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Abstract

This paper proposed a novel adaptive tracking neural network with deadzone robust compensator for Industrial Robot Manipulators (IRMs) to achieve the high precision position tracking performance. In order, to deal the uncertainty, the unknown deadzone effect, the unknown dynamics, and disturbances of robot system, the Radial Basis function neural networks (RBFNNs) control is presented to control the joint position and approximate the unknown dynamics of an n-link robot manipulator. The online adaptive control training laws and estimation of the dead-zone are determined by Lyapunov stability and the approximation theory, so that the stability of the entire system and the convergence of the weight adaptation are guaranteed. In this controller, a robust compensator is constructed as an auxiliary controller to guarantee the stability and robustness under various environments such as the mass variation, the external disturbances and modeling uncertainties. The proposed control is the verified on a three-joint robot manipulators via simulations and experiments in comparison with PID and Neural networks (NNs) control.

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Author information

Authors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, Hunan Changsha, China

    La Van Truong & Shou Dao Huang

  2. Faculty of Electrical-Electronic Engineering, NamDinh University of Technology Education, NamDinh, Vietnam

    La Van Truong

  3. Faculty of Electrical Engineering Technology, Hanoi University of Industry, Hanoi, Vietnam

    Vu Thi Yen & Pham Van Cuong

Authors
  1. La Van Truong
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  2. Shou Dao Huang
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  3. Vu Thi Yen
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  4. Pham Van Cuong
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Correspondence to Pham Van Cuong.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Associate Editor Yang Tang under the direction of Editor Hamid Reza Karimi.

La Van Truong received his Bachelor’s degree in Automation Engineering from the Thai Nguyen University and Master’s degree from Department of Automatic Control from Military Technical Academy, Vietnam, in 2007 and 2010, respectively. His research interests include robot control, neural network, adaptive control and optimal control. He with the Faculty of Electrical & Electronics Engineering, Nam Dinh University of technology education, Nam Dinh, Vietnam.

Shou Dao Huang was born in Hunan, China, in 1962. He received his B.S. and Ph.D. degrees from the College of Electrical and Information Engineering, Hunan University, Changsha, China, in 1983 and 2005, respectively. He is currently a Full Professor with the College of Electrical and Information Engineering, Hunan University. His current research interests include motor design and control, power electronic system and control, and wind energy conversion system.

Vu Thi Yen received her B.S. and M.S. degrees in Automation Engineering from the Thai Nguyen University, College of Engineering, Vietnam, in 2008 and 2011, respectively, and a Ph.D. degree in Control Science and Engineering from Hunan University, Changsha, China, in 2019. She joined the Faculty of Electrical Engineering as a Lecturer in Hanoi University of Industry, Hanoi, Viet Nam since 2020. Her research interests include Robot control, Fuzzy neural network, and robust control.

Pham Van Cuong received his Bachelor’s and Master’s degrees from Department of Automatic Control, Military Technical Academy, Vietnam, in 2007 and 2010, respectively, and his Ph.D. degree in Control Science and Engineering from Hunan University, Changsha, China, in 2015. He joined the Faculty of Electrical Engineering Technology as a Lecturer in Hanoi University of Industry, Hanoi, Viet Nam since 2003. His research interests include intelligent control theory, adaptive control, robust control, applications and robot manipulators.

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Truong, L.V., Huang, S.D., Yen, V.T. et al. Adaptive Trajectory Neural Network Tracking Control for Industrial Robot Manipulators with Deadzone Robust Compensator. Int. J. Control Autom. Syst. 18, 2423–2434 (2020). https://doi.org/10.1007/s12555-019-0513-7

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  • DOI: https://doi.org/10.1007/s12555-019-0513-7

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