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Event-triggered finite-time fuzzy control approach for fractional-order nonlinear chaotic systems with input delay

Author

Listed:
  • Lin, Funing
  • Xue, Guangming
  • Qin, Bin
  • Li, Shenggang
  • Liu, Heng

Abstract

In this paper, a novel fuzzy event-triggered control approach with guaranteed performance of practical finite-time tracking convergence is constructed for uncertain fractional-order nonlinear chaotic systems in the presence of time-varying input delay. Firstly, fuzzy logic systems are employed to deal with immeasurable systematic information. Secondly, fractional-order command filters are incorporated to circumvent the problem of “complexity explosion” in backstepping controller construction. Meanwhile, a sequence of fractional-order finite-time compensation signals are introduced to counteract the adverse influence of time-varying input delay rapidly. Furthermore, a novel practical finite-time chaos control approach based on event-triggered strategy is proposed, which not only facilitates the decrease of sampling frequency and controller burden dynamically, but also ensures the convergence of all tracking errors towards a small region around the origin in finite time. Finally, through the simulation research, the transient response performance and the chaos-suppression ability of fractional-order chaotic systems stabilized via the suggested approach are rigorously testified.

Suggested Citation

  • Lin, Funing & Xue, Guangming & Qin, Bin & Li, Shenggang & Liu, Heng, 2023. "Event-triggered finite-time fuzzy control approach for fractional-order nonlinear chaotic systems with input delay," Chaos, Solitons & Fractals, Elsevier, vol. 175(P2).
    • Handle: RePEc:eee:chsofr:v:175:y:2023:i:p2:s0960077923009372
    DOI: 10.1016/j.chaos.2023.114036
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    References listed on IDEAS

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    2. Alsaadi, Fuad E. & Alharbi, Njud S. & Al-Barakati, Abdullah A., 2026. "Nonlinear dynamics and uncertainty-aware control of prosthetic systems using Bayesian Neural Networks and finite-time disturbance compensation," Chaos, Solitons & Fractals, Elsevier, vol. 202(P2).
    3. Zhao, Xiao-Wen & Deng, Dong-Dong & Ge, Ming-Feng & Liu, Zhi-Wei, 2024. "Neural adaptive predefined-time formation tracking control of multiple Euler–Lagrange systems with switching topologies based on hierarchical mechanism," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
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    5. Manh Hung Nguyen & Kyoung Kwan Ahn, 2024. "A Finite-Time Disturbance Observer for Tracking Control of Nonlinear Systems Subject to Model Uncertainties and Disturbances," Mathematics, MDPI, vol. 12(22), pages 1-16, November.

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