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Event-triggered fuzzy learning control for uncertain robotic manipulators via gradient descent approach

Author

Listed:
  • Ma, Min
  • Lin, Zefeng
  • Zhao, Zhihong
  • Wang, Tong
  • Sui, Shuai

Abstract

This paper develops an event-triggered fuzzy learning control scheme for robotic manipulators subject to uncertain dynamics and unknown control gains. A novel gradient descent strategy is put forward to balance the trade-off between the event-triggered updating of motor driving signals and tracking performance, by which the control design dependence on prior knowledge from manipulator dynamics is simultaneously alleviated. The constructed gradient descent-based fuzzy learning mechanism improves the approximation accuracy of fuzzy logic systems (FLSs) to uncertain manipulator dynamics. And an error compensating-based command filter design technique is introduced to reduce the computation burden. Lyapunov analysis theory rigorously proves the semi-global uniform ultimate boundedness (SUUB) of the closed-loop system. In the end, a simulation case and some comparison results are illustrated to demonstrate the efficacy of the proposed gradient descent-based event-triggered control scheme.

Suggested Citation

  • Ma, Min & Lin, Zefeng & Zhao, Zhihong & Wang, Tong & Sui, Shuai, 2026. "Event-triggered fuzzy learning control for uncertain robotic manipulators via gradient descent approach," Chaos, Solitons & Fractals, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:chsofr:v:203:y:2026:i:c:s0960077925016856
    DOI: 10.1016/j.chaos.2025.117672
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    References listed on IDEAS

    as
    1. Gao, Jinfeng & Tan, Zhonghao & Li, Lebao & Jia, Guoqiang & Liu, Peter Xiaoping, 2025. "A novel finite-time non-singular robust control for robotic manipulators," Chaos, Solitons & Fractals, Elsevier, vol. 194(C).
    2. Zhang, Tianping & Zhang, Wei, 2024. "Adaptive practical prescribed-time control for uncertain nonlinear systems with time-varying parameters," Chaos, Solitons & Fractals, Elsevier, vol. 189(P1).
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