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High-Order Sliding Mode Control for Three-Joint Rigid Manipulators Based on an Improved Particle Swarm Optimization Neural Network

Author

Listed:
  • Jin Zhang

    (School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
    School of Intelligent Manufacturing, Shanxi Vocational University of Engineering Science and Technology, Jinzhong 030619, China)

  • Wenjun Meng

    (School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
    Shanxi Institute of Energy, Taiyuan 030600, China)

  • Yufeng Yin

    (School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China)

  • Zhengnan Li

    (School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
    Shanxi Provincial Engineering Research Center for Intelligent Heavy Load Equipment & Robot System, Taiyuan 030024, China)

  • Lidong Ma

    (School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
    Shanxi Provincial Engineering Research Center for Intelligent Heavy Load Equipment & Robot System, Taiyuan 030024, China)

  • Weiqiang Liang

    (School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
    School of Intelligent Manufacturing, Shanxi Vocational University of Engineering Science and Technology, Jinzhong 030619, China)

Abstract

This paper presents a control method for the problem of trajectory jitter and poor tracking performance of the end of a three-joint rigid manipulator. The control is based on a high-order particle swarm optimization algorithm with an improved sliding mode control neural network. Although the sliding mode variable structure control has a certain degree of robustness, because of its own switching characteristics, chattering can occur in the later stage of the trajectory tracking of the manipulator end. Hence, on the basis of the high-order sliding mode control, the homogeneous continuous control law and super-twisting adaptive algorithm were added to further improve the robustness of the system. The radial basis function neural network was used to compensate the errors in the modeling process, and an adaptive law was designed to update the weights of the middle layer of the neural network. Furthermore, an improved particle swarm optimization algorithm was established and applied to optimize the parameters of the neural network, which improved the trajectory tracking of the manipulator end. Finally, MATLAB simulation results indicated the validity and superiority of the proposed control method compared with other sliding mode control algorithms.

Suggested Citation

  • Jin Zhang & Wenjun Meng & Yufeng Yin & Zhengnan Li & Lidong Ma & Weiqiang Liang, 2022. "High-Order Sliding Mode Control for Three-Joint Rigid Manipulators Based on an Improved Particle Swarm Optimization Neural Network," Mathematics, MDPI, vol. 10(19), pages 1-22, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3418-:d:919845
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    References listed on IDEAS

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    1. Carlos Lopez-Franco & Dario Diaz & Jesus Hernandez-Barragan & Nancy Arana-Daniel & Michel Lopez-Franco, 2022. "A Metaheuristic Optimization Approach for Trajectory Tracking of Robot Manipulators," Mathematics, MDPI, vol. 10(7), pages 1-23, March.
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    Cited by:

    1. Chen, Jiqing & Zhang, Haiyan & Zhu, Tongtong & Pan, Shangtao, 2024. "Trajectory tracking control of a manipulator based on an immune algorithm-optimized neural network in the presence of unknown backlash-like hysteresis," Applied Mathematics and Computation, Elsevier, vol. 470(C).

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