Observer-Based Adaptive Fuzzy Tracking Control Using Integral Barrier Lyapunov Functionals for A Nonlinear System With Full State Constraints

This paper aims at the tracking control problem of the non-strict-feedback non-linear systems with non-constant unknown delays and full state constraints. A constrained exponential Lyapunov-Krasovskii function-based fuzzy adaptive dynamic surface control (CELK-FADSC) method is proposed. In order to achieve the goal of full state constraints, the tan-type Barrier Lyapunov function (BLF) is used. Furthermore, to reduce the influence of the non-constant unknown delays on the system, an exponential Lyapunov-Krasovskii function is constructed. At the same time, sliding mode differentiator is adopted to the dynamic surface control (DSC) method, by which the 'complexity explosion' problem in backstepping is solved. In addition, the proposed adaptive tracking controller can ensure that the states in the closed-loop system do not violate their constraint, and the semi-global uniformly ultimately bounded (SGUUB) can be achieved. Finally, simulation outcomes can prove the validity of the proposed method.

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“…Lemma 2 [30]. If Υ(x) is a continuous and bounded function defined on a compact set Z , for ∀𝜀 > 0, there is a FLS that satisfies…”

Section: Fuzzy Logic Systemmentioning

confidence: 99%

Adaptive fuzzy tracking control for non‐strict‐feedback non‐linear systems with non‐constant delays and full state constraints

Zhuang

Chen

Zhang

et al. 2022

IET Control Theory & Appl

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“…Projected consensus algorithms have been adopted to confine states to a convex set for multiagent networks [23]- [25]. An integral barrier Lyapunov function has been used in [26] to implement fuzzy tracking control under full state constraints. A survey of cooperative coordination with constraints can be found in [27].…”

Section: Introductionmentioning

confidence: 99%

Scaled Consensus and Reference Tracking in Multiagent Networks With Constraints

Shang

2022

IEEE Trans. Netw. Sci. Eng.

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“…As known, FTC can be divided into two main groups, that is, passive fault-tolerant control (PFTC) 19,20 and active fault-tolerant control (AFTC). [21][22][23] In the PFTC, the fault is usually considered as the appearance of disturbance, and the robust H ∞ control [24][25][26] or other control schemes [27][28][29][30][31][32] are utilized to make the system insensitive to faults. On the other hand, the AFTC frame usually takes the information from fault detection and isolation (FDI) block, 33 and the corresponding control laws are designed to compensate for the effects of the faults, which can achieve better performance in comparison to the PFTC strategy.…”

Section: Introductionmentioning

confidence: 99%

Active fault tolerant control design using switching linear parameter varying controllers with inexact fault‐effect parameters

Che

Zhu

Zhou

et al. 2022

Intl J Robust & Nonlinear

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In this article, a novel active fault-tolerant control (AFTC) design method is developed by using the switching linear parameter varying (LPV) controller with inexact fault-effect parameters. The LPV controller is not only considered as a gain-scheduled controller varying with the operating points, but also an AFTC controller varying with the fault-effect parameters, which can be obtained via the fault estimator in real-time. The inexact measurement of fault-effect parameters is considered for the first time in this article. To cover large parameters variation, a class of switched LPV fault-tolerant controller is designed to work in the multiple partitioned parameters subregions. The dynamic output feedback controller model is constructed to perform the switched LPV fault-tolerant control task under the mode-dependent average dwell time constraint. By using the multiple parameter-dependent Lyapunov function approach, sufficient conditions with less conservatism are obtained, such that the corresponding closed-loop systems are globally uniformly exponentially stable and satisfy an upper bound of weighted l 2 -gain performance indexes. Finally, effectiveness and applicability of the developed approaches are validated via an active magnetic bearing system.

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Observer-Based Adaptive Fuzzy Tracking Control Using Integral Barrier Lyapunov Functionals for A Nonlinear System With Full State Constraints

Cited by 55 publications

References 38 publications

Adaptive fuzzy tracking control for non‐strict‐feedback non‐linear systems with non‐constant delays and full state constraints

Adaptive fuzzy tracking control for non‐strict‐feedback non‐linear systems with non‐constant delays and full state constraints

Scaled Consensus and Reference Tracking in Multiagent Networks With Constraints

Active fault tolerant control design using switching linear parameter varying controllers with inexact fault‐effect parameters

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