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Control Method of Flexible Manipulator Servo System Based on a Combination of RBF Neural Network and Pole Placement Strategy

Author

Listed:
  • Dongyang Shang

    (School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China)

  • Xiaopeng Li

    (School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China)

  • Meng Yin

    (Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China)

  • Fanjie Li

    (School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China)

Abstract

Gravity and flexibility will cause fluctuations of the rotation angle in the servo system for flexible manipulators. The fluctuation will seriously affect the motion accuracy of end-effectors. Therefore, this paper adopts a control method combining the RBF (Radial Basis Function) neural network and pole placement strategy to suppress the rotation angle fluctuations. The RBF neural network is used to identify uncertain items caused by the manipulator’s flexibility and the time-varying characteristics of dynamic parameters. Besides, the pole placement strategy is used to optimize the PD (Proportional Differential) controller’s parameters to improve the response speed and stability. Firstly, a dynamic model of flexible manipulators considering gravity is established based on the assumed mode method and Lagrange’s principle. Then, the system’s control characteristics are analyzed, and the pole placement strategy optimizes the parameters of the PD controllers. Next, the control method based on the RBF neural network is proposed, and the Lyapunov stability theory demonstrates stability. Finally, numerical analysis and control experiments prove the effectiveness of the control method proposed in this paper. The means and standard deviations of rotation angle error are reduced by the control method. The results show that the control method can effectively reduce the rotation angle error and improve motion accuracy.

Suggested Citation

  • Dongyang Shang & Xiaopeng Li & Meng Yin & Fanjie Li, 2021. "Control Method of Flexible Manipulator Servo System Based on a Combination of RBF Neural Network and Pole Placement Strategy," Mathematics, MDPI, vol. 9(8), pages 1-28, April.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:8:p:896-:d:538161
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    Citations

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    Cited by:

    1. Xiangchen Liu & Minghai Wang & Yihan Wang, 2023. "Improved Vibration Suppression Strategy of Fuzzy PI Servo Control for Dual Flexible System with Flexible Joints," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
    2. Xiangchen Liu & Yihan Wang & Minghai Wang, 2022. "Speed Fluctuation Suppression Strategy of Servo System with Flexible Load Based on Pole Assignment Fuzzy Adaptive PID," Mathematics, MDPI, vol. 10(21), pages 1-17, October.

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