Predefined-Time Adaptive Neural Tracking Control of Switched Nonlinear Systems

Wang, Huanqing; Tong, Miao; Zhao, Xudong; Niu, Ben; Yang, Man

doi:10.1109/tcyb.2022.3204275

Cited by 62 publications

(25 citation statements)

References 52 publications

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“…The switching rules are also called switching signals which decide the switching manner between the different controlled subsystems. Due to the arbitrary switching performance of the switching signals, a large number of interesting results for switched systems have been reported in References 4‐14. Some scholars adopted the average dwell time approach to design the actual controller for the nonlinear switching systems in References 4‐7.…”

Section: Introductionmentioning

confidence: 99%

“…Some scholars adopted the average dwell time approach to design the actual controller for the nonlinear switching systems in References 4‐7. Others proposed the adaptive control schemes for switching systems by introducing the common Lyapunov functions (CLFs) in References 8‐14. The understanding of the CLFs is that a switched system can be guaranteed to be asymptotically stable if and only if all subsystems depend on a common Lyapunov function.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive fuzzy fixed‐time control for non‐triangular structural stochastic switching nonlinear systems

Wang

Liu

2023

Intl J Robust & Nonlinear

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In this article, the problem of adaptive fixed‐time tracking control is addressed for non‐triangular structural stochastic switching nonlinear systems. Fuzzy logic systems are used to compensate for unknown nonlinearities, and in the meantime, to avoid the algebraic loop problem, which exists in traditional backstepping controller design processes for non‐triangular structural nonlinear systems. The unknown control gain problem is addressed by the construction of proper Lyapunov function candidates. By employing Lyapunov stability theorem, all the closed‐loop signals can be ensured to be semi‐globally practical fixed‐time stable, the output signal can track the desired signal, and the tracking error can converge to a small zone around the origin. The effectiveness of the developed control approach is verified through simulation results.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive fuzzy fixed‐time control for non‐triangular structural stochastic switching nonlinear systems

Wang

Liu

2023

Intl J Robust & Nonlinear

Self Cite

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“…A finite‐time fuzzy command filter control design for stochastic nonlinear systems with input quantization was carried out by Kang et al 18 Furthermore, the above studies on the fast FTS problem cannot deal with completely unknown non‐linear functions. Fortunately, fuzzy logic systems and neural networks could have been confirmed to be valid appliances to approximate nonlinear functions 19‐29 . For instance, Reference 19 investigates global adaptive control based on fuzzy approximation 19 .…”

Section: Introductionmentioning

confidence: 99%

“…Fortunately, fuzzy logic systems and neural networks could have been confirmed to be valid appliances to approximate nonlinear functions. [19][20][21][22][23][24][25][26][27][28][29] For instance, Reference 19 investigates global adaptive control based on fuzzy approximation. 19 Cui et al 28 researched adaptive fuzzy control for MIMO nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Fast finite‐time adaptive tracking control for non‐strict feedback stochastic nonlinear systems with full state constraints

Zhao,

Sun

et al. 2023

Adaptive Control & Signal

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SummaryThis article focuses on the fast finite‐time adaptive tracking control for non‐strict feedback stochastic nonlinear systems. Random disturbances with constraints have rarely been considered in previous studies of fast finite‐time stabilization problems. The corresponding extension of the adaptive fast finite‐time stabilization mechanism to stochastic nonlinear systems with full‐state constraints is one of the innovations of this article. First, neural networks and barrier Lyapunov functions are utilized to handle the problems arising from the unknown nonlinear terms and the state constraints, respectively. Second, an adaptive fast finite‐time tracking control scheme for stochastic systems is designed based on the backstepping technique and a fast finite‐time control strategy, which allows all signals of the closed‐loop system to be bounded. Meanwhile, the designed controller guarantees a fast convergence of the tracking error to a small neighborhood of the origin under full‐state constraints. Finally, two examples are given to illustrate the feasibility of the proposed method.

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“…On the other hand, a neural network (NN)‐aided adaptive observer is developed for the observation of actuator faults 6 . In Reference 13 adaptive control, backstepping technique, and finite‐time stability theory, an adaptive finite‐time tracking controller is developed with unmodeled dynamics and dynamic disturbances. A robust fault‐tolerant control protocol proposed in Reference 14 that takes the effect of time‐varying actuator faults and actuator saturation is considered for finite‐time leader‐following consensus of nonlinear discrete‐time MAS with Markov jump parameters.…”

Section: Introductionmentioning

confidence: 99%

Reinforced unscented Kalman filter for consensus achievement of uncertain multi‐agent systems subject to actuator faults

Borah,

Kumar

2023

Intl J Robust & Nonlinear

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In this paper, actuator fault detection and reconstruction in consensus tracking of uncertain multi‐agent systems (MAS) is addressed. The communication is assumed to be connected undirected. An adaptive fault detection method is developed to detect actuator faults. A novel‐reinforced unscented Kalman filter (RUKF) is employed to reconstruct the faults by adjusting the noise covariance matrices of unscented Kalman filter (UKF) as well as to train neural network internal parameters by providing a set of previous measurements. A Chebyshev neural network (CNN) is incorporated to learn the uncertain plant. To prevent the neural network approximation errors a hyperbolic tangent function‐based robust control term is applied. The Lyapunov stability approach guarantees the stability of the proposed RUKF, which runs in conjunction with robust control method. Lastly, numerical simulations are presented to show the effectiveness of the proposed RUKF under actuator abrupt, intermittent, and transient fault conditions.

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Predefined-Time Adaptive Neural Tracking Control of Switched Nonlinear Systems

Cited by 62 publications

References 52 publications

Adaptive fuzzy fixed‐time control for non‐triangular structural stochastic switching nonlinear systems

Adaptive fuzzy fixed‐time control for non‐triangular structural stochastic switching nonlinear systems

Fast finite‐time adaptive tracking control for non‐strict feedback stochastic nonlinear systems with full state constraints

Reinforced unscented Kalman filter for consensus achievement of uncertain multi‐agent systems subject to actuator faults

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