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Prescribed Fixed-Time Adaptive Neural Control for Manipulators with Uncertain Dynamics and Actuator Failures

Author

Listed:
  • Guanyu Lai

    (School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Sheng Zhou

    (School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Weijun Yang

    (School of Mechanical and Electrical Engineering, Guangzhou City Polytechnic, Guangzhou 510405, China)

  • Xiaodong Wang

    (School of Mechanical and Electrical Engineering, Guangzhou City Polytechnic, Guangzhou 510405, China)

  • Fang Wang

    (College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao 266590, China)

Abstract

In this paper, a fixed-time adaptive neural control scheme is proposed to solve the prescribed tracking problem of robot manipulators in the presence of uncertain dynamics, and stuck-type actuator failures which are unknown in time, pattern, and values. Technically, the combination of neural networks and adaptive control is used to handle the uncertainties in system dynamics, an adaptive compensation mechanism is designed to accommodate the failures occurring in actuators, and also a systematic design procedure based on the prescribed performance bounds is presented to establish the conditional inequality for ensuring fixed-time stability. With our scheme, it can be proved rigorously that the tracking errors in joint space can always be kept within the prescribed bounds, and converge to a small region of zero in a bounded settling time, in addition to the closed-loop signal boundedness. The proposed scheme is validated through simulations.

Suggested Citation

  • Guanyu Lai & Sheng Zhou & Weijun Yang & Xiaodong Wang & Fang Wang, 2023. "Prescribed Fixed-Time Adaptive Neural Control for Manipulators with Uncertain Dynamics and Actuator Failures," Mathematics, MDPI, vol. 11(13), pages 1-20, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2925-:d:1183068
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    References listed on IDEAS

    as
    1. Jun Yang & Jing Na & Guanbin Gao & Chao Zhang, 2018. "Adaptive Neural Tracking Control of Robotic Manipulators with Guaranteed NN Weight Convergence," Complexity, Hindawi, vol. 2018, pages 1-11, October.
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