Adaptive Fuzzy Fixed-Time Control for High-Order Nonlinear Systems With Sensor and Actuator Faults

Wang, Huanqing; Ma, Jiawei; Zhao, Xudong; Niu, Ben; Chen, Ming; Wang, Wei

doi:10.1109/tfuzz.2023.3235395

Cited by 27 publications

(9 citation statements)

References 40 publications

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“…Example 1. To illustrate the efficacy of the proposed approach, we introduce a single-link robotic manipulator system 51 :…”

Section: Simulationmentioning

confidence: 99%

Adaptive prescribed performance tracking control of high‐order uncertain nonlinear systems under full‐state time‐varying constraints

Chang,

Bai,

Jiao

et al. 2024

Intl J Robust & Nonlinear

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In this article, the adaptive finite time prescribed performance tracking control problem of a class of high‐order uncertain nonlinear systems under full state time‐varying constraints is addressed. By using the logarithmic function based nonlinear mapping technique rather than the barrier Lyapunov function based method, the obstacle of the full‐state constraints is resolved, and the undesired “feasibility conditions” in controller design are eliminated. By improving the prescribed performance control method, the initial value problem is overcome, and the finite‐time convergence of the tracking error can be ensured. Then, with the aid of the adding a power integrator method, an adaptive finite time prescribed performance tracking controller is designed, not only the tracking error but also the full states can be guaranteed to converge to a predefined constraint region in the predetermined time. Simulation examples are given to show the validness of the proposed control strategy.

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“…Example 1. To illustrate the efficacy of the proposed approach, we introduce a single-link robotic manipulator system 51 :…”

Section: Simulationmentioning

confidence: 99%

Adaptive prescribed performance tracking control of high‐order uncertain nonlinear systems under full‐state time‐varying constraints

Chang,

Bai,

Jiao

et al. 2024

Intl J Robust & Nonlinear

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“…Consider the non-affine nonlinear systems (1) with directed connected graph 𝜍 satisfying Assumptions 1, 2 and Lemma 1. The virtual controllers (20), (30), and (39), the desired input (50) are designed to obtain a good tracking performance, all closed-loop signals are bounded and all the states stay in corresponding constraint intervals.…”

Section: Distributed Controller Designmentioning

confidence: 99%

“…Adaptive algorithms are used in linear and nonlinear systems. Adaptive algorithms have a lot of experience in linear systems, 20,21 for example, References 22-26. The solution of nonlinear systems is complex, so the nonlinear systems are approximated by adaptive neural network.…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural network control of non‐affine multi‐agent systems with actuator fault and input saturation

Yuan,

Liu,

Liu

et al. 2023

Intl J Robust & Nonlinear

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An adaptive neural network fault‐tolerant control method is proposed for the non‐affine multi‐agent systems with actuator failure and input saturation. Through the transformation of the original multi‐agent systems, an equivalent model is proposed. Considering that the signal input of multi‐agent systems is limited, this article will solve the input saturation problem. But in a real system, the actuator would probably fail. Therefore, a fault tolerant control algorithm is proposed to solve the fault problem in the systems. In this article, an adaptive distributed controller is designed to eliminate the uncertainty of the systems. At the same time, the integral barrier Lyapunov function is selected to verify the stability. Finally, a simulation example is given to demonstrate the effectiveness of the proposed method.

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“…A great deal of significant achievements have been published in the study of systems with unknown nonlinearity. In the literature [16,[25][26][27][28][29] and [30][31][32][33], the authors proposed many adaptive control strategies for nonlinear switching systems and nonlinear multi-intelligent systems by using neural networks (fuzzy logic systems) and the backstepping method, respectively. Reference [34] investigated the problem of adaptive fuzzy fixed-time control for a class of nonlinear systems subject to time-varying full state constraints and actuator hysteresis.…”

Section: Introductionmentioning

confidence: 99%

Nussbaum gain adaptive fuzzy control for switched nonlinear systems with predefined output and full time‐varying states constraints

Ding

et al. 2023

Asian Journal of Control

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In this paper, the problem of adaptive fuzzy tracking control for a class of uncertain switched nonlinear systems with unknown control direction is studied. Aiming at the problem, an adaptive control scheme with Nussbaum gain technology is constructed by using the average dwell time (ADT) method and the backstepping method to overcome the unknown control direction, and time‐varying asymmetric barrier Lyapunov functions (ABLFs) are adopted to ensure the full‐state constraints satisfaction. The proposed control scheme guarantees that all closed‐loop signals remain bounded under a class of switching signals with ADT, while the output tracking error converges to a small neighborhood of the zero. An important innovation of this design method is that the unknown control direction, asymmetric time‐varying full state constraints, and predefined time‐varying output requirements are simultaneously considered in uncertain switched nonlinear systems for the first time. We set a moment in advance, and make the systems comply with the constraint conditions before running the moment by the shift function nested in the first time‐varying ABLF. Finally, a simulation example verifies the effectiveness of the proposed scheme.

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Adaptive Fuzzy Fixed-Time Control for High-Order Nonlinear Systems With Sensor and Actuator Faults

Cited by 27 publications

References 40 publications

Adaptive prescribed performance tracking control of high‐order uncertain nonlinear systems under full‐state time‐varying constraints

Adaptive prescribed performance tracking control of high‐order uncertain nonlinear systems under full‐state time‐varying constraints

Adaptive neural network control of non‐affine multi‐agent systems with actuator fault and input saturation

Nussbaum gain adaptive fuzzy control for switched nonlinear systems with predefined output and full time‐varying states constraints

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