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Improved Adaptive Fuzzy Control for Non-Strict Feedback Nonlinear Systems: a Dynamic Compensation System Approach

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  • Wu, Dawei
  • Sun, Yonghui
  • Xia, Rongsheng
  • Lu, Shumin

Abstract

This paper investigates the tracking control problem for uncertain nonlinear non-strict feedback systems (NSFSs) in the presence of full-state constraints and unmeasured disturbances. It is of great practical significance to realize the full-state constraint under disturbed conditions. In view of the non-strict feedback problem, a novel design framework of the state feedback control is given based on the newly proposed dynamic compensation system (DCS). Different from the traditional backstepping, the estimated signal based on the adaptive fuzzy system is indirectly introduced into the virtual and actual control laws through the DCS, which has the advantage of avoiding the algebraic-loop problem in NSFSs. Accordingly, the disturbance observer (DO) design method is improved based on this framework. Integrating the DCS into the DO design avoids the coupling problem between the disturbance and the unknown nonlinear function. An improved barrier Lyapunov function (BLF) is designed by introducing the concept of the inducible factor, and full-state constraints can be guaranteed after a transitional period for any initial state. By combining the DCS, the DO and the improved BLF, a novel adaptive fuzzy tracking control law is constructed, and all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. Finally, the theoretical analysis and simulation results show that all states meet the corresponding constraints while meeting the stability.

Suggested Citation

  • Wu, Dawei & Sun, Yonghui & Xia, Rongsheng & Lu, Shumin, 2022. "Improved Adaptive Fuzzy Control for Non-Strict Feedback Nonlinear Systems: a Dynamic Compensation System Approach," Applied Mathematics and Computation, Elsevier, vol. 435(C).
    • Handle: RePEc:eee:apmaco:v:435:y:2022:i:c:s0096300322005446
    DOI: 10.1016/j.amc.2022.127470
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    References listed on IDEAS

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    1. Yongchao Liu & Qidan Zhu, 2022. "Adaptive fuzzy asymptotic control for switched nonlinear systems with state constraints," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(5), pages 922-933, April.
    2. Jin-Zi Yang & Yuan-Xin Li, 2020. "Fuzzy adaptive asymptotic tracking of uncertain nonlinear systems with full states constraints," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(16), pages 3550-3562, December.
    3. Yingxue Hou & Shaocheng Tong & Yongming Li, 2016. "Adaptive fuzzy backstepping control for a class of switched nonlinear systems with actuator faults," International Journal of Systems Science, Taylor & Francis Journals, vol. 47(15), pages 3581-3590, November.
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    Cited by:

    1. Truong, Hoai Vu Anh & Phan, Van Du & Tran, Duc Thien & Ahn, Kyoung Kwan, 2024. "A novel observer-based neural-network finite-time output control for high-order uncertain nonlinear systems," Applied Mathematics and Computation, Elsevier, vol. 475(C).

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