IBLF-Based Finite-Time Adaptive Fuzzy Output-Feedback Control for Uncertain MIMO Nonlinear State-Constrained Systems

This article considers the problem of finite-time adaptive fuzzy output-feedback control design for multi-input-multioutput uncertain nonlinear systems subject to full state constraints. By employing the finite-time stability theory, a new finite-time adaptive fuzzy output-feedback control approach...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems 2021-11, Vol.29 (11), p.3389-3400
Main Authors: Wei, Yan, Wang, Yueying, Ahn, Choon Ki, Duan, Dengping
Format: Article
Language:English
Subjects:
Adaptive control
Adaptive fuzzy control
Adaptive systems
Backstepping
Constraints
Control stability
Convergence
Feedback control
finite time
Fuzzy control
Fuzzy logic
Fuzzy systems
integral barrier Lyapunov function (iBLF)
Lyapunov methods
MIMO communication
Nonlinear systems
Output feedback
Stability analysis
state constraints
State observers
Tracking errors
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Summary:This article considers the problem of finite-time adaptive fuzzy output-feedback control design for multi-input-multioutput uncertain nonlinear systems subject to full state constraints. By employing the finite-time stability theory, a new finite-time adaptive fuzzy output-feedback control approach is proposed. An integral barrier Lyapunov functional is utilized to prevent all states from violating their constraints. Fuzzy logic systems are developed to approximate the uncertainties. A fuzzy state observer is constructed to estimate the unmeasurable states. Moreover, to handle the "explosion of complexity" issue in the backstepping control technique, a finite-time convergent differentiator is introduced to estimate the time derivatives of virtual control signals. The stability analysis showed that the control approach guarantees that all closed-loop signals are bounded, and the tracking errors converge to a small neighborhood of the origin in a finite time. Finally, the effectiveness of the proposed control scheme is confirmed by numerical simulations.
ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2020.3021733