Command filter-based adaptive fuzzy decentralized control for large-scale nonlinear systems

Kang, Shijia; Liu, Peter; Wang, Huanqing

doi:10.1007/s11071-021-06779-4

Cited by 20 publications

(12 citation statements)

References 50 publications

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“…Remark Assumptions 1 and 2 are used to compensate for interconnected, stochastic, and time‐delay items in the backstepping design framework by inequality scaling. Similar assumptions can be found in the previous work 5,41,42,44 …”

Section: Preliminary Knowledge and Problem Formulationsupporting

confidence: 68%

“…Similar assumptions can be found in the previous work. 5,41,42,44 For the available and bounded y (i) j,d (t), the main objective is to construct an adaptive neural fault-tolerant decentralized controller for the switched stochastic nonlinear large-scale system (3) such that: (1) for the desired trajectory y j,d (t), the tracking error z j,1 (t) = y j (t) − y j,d (t) converges to a small neighborhood of the origin; (2) all the signals in the closed-loop system remain bounded under arbitrary switching.…”

Section: Problem Formulationmentioning

confidence: 99%

“…When both stochastic term and delay term are zero, the switched stochastic nonlinear large-scale system (3) degenerates to the system. 4,5 In other words, the system considered in this article is more complex and generalized.…”

Section: Problem Formulationmentioning

confidence: 99%

“…In the era of information, there have been plenty of large-scale systems composed of many subsystems connected by coupling terms such as mechanical systems, power systems, cyber-physical systems, and so on. [1][2][3][4][5][6][7][8][9] For example, by merging the adaptive neural control 10 and decentralized control method, 1 an adaptive neural decentralized control strategy was proposed, 9 in which the neural network (NN) was applied to approximate the unknown nonlinear function. On the other hand, as a typical hybrid dynamic system, [11][12][13][14][15][16][17][18][19][20][21] switched system which includes multiple subsystems and switching laws, is able to simulate more practical systems than the non-switched system.…”

Section: Introductionmentioning

confidence: 99%

“…In the era of information, there have been plenty of large‐scale systems composed of many subsystems connected by coupling terms such as mechanical systems, power systems, cyber‐physical systems, and so on 1‐9 . For example, by merging the adaptive neural control 10 and decentralized control method, 1 an adaptive neural decentralized control strategy was proposed, 9 in which the neural network (NN) was applied to approximate the unknown nonlinear function.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Adaptive fault‐tolerant decentralized tracking control of switched stochastic uncertain nonlinear systems with time‐varying delay

Cui

Zou

Guo

et al. 2022

Adaptive Control & Signal

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In this article, an adaptive neural fault-tolerant decentralized tracking control problem is investigated for switched stochastic large-scale uncertain nonlinear systems, in which time-varying delay and unknown control coefficient are under consideration. By incorporating the quadratic Lyapunov function and Lyapunov Krasovskii function into the backstepping approach, an adaptive neural fault-tolerant decentralized control strategy is proposed, which ensures that signals in the closed-loop system are stable in probability and the tracking error converges to a small neighborhood of the origin under arbitrary switching. At last, to verify the presented strategy is feasible, a simulation example is given.

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Section: Preliminary Knowledge and Problem Formulationsupporting

confidence: 68%

Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive fault‐tolerant decentralized tracking control of switched stochastic uncertain nonlinear systems with time‐varying delay

Cui

Zou

Guo

et al. 2022

Adaptive Control & Signal

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Fuzzy finite‐time adaptive control of switched nonlinear systems with input nonlinearities

Wang,

Tong

2024

Adaptive Control & Signal

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SummaryThis article addresses the fuzzy finite‐time command filtering tracking control problem for switched nonlinear systems with input nonlinearities via using backstepping technique. An equivalent transformation method is proposed for the purpose of handling the impediment problem caused by the nonlinearity of control input. Finite‐time command filtering control technology can solve the “explosion of complexity” issue, which is caused by the multiple derivation of virtual controllers appeared in the classic backstepping control. A filtering compensation scheme is developed to reduce the filtering error. By combining the fuzzy logic system's approximation ability and the finite‐time theory, a new fast convergence adaptive control scheme is proposed so that the output of the system can converge to a small area near the desired signal and the boundedness of all signals of the controlled system can be assured in finite time. Finally, a simulation example successfully verifies the feasibility of the developed control scheme.

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Adaptive neural practical fixed‐time command filtered control for multi‐input and multi‐output nonlinear systems with dead zones input and unknown control direction

Kang,

Xiaoping Liu,

Wang

2023

Asian Journal of Control

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In this article, under the circumstance of dead zones input and unknown control direction, the adaptive practical fixed‐time control strategy is presented for a general class of multi‐input and multi‐output (MIMO) nonlinear systems. The inherent explosion of computational complexity difficulty is eliminated by adopting a command filter technique and the universal approximation properties of radial basis function neural networks (RBFNNs) are applied to model the unknown nonlinear functions. The difficulties of the dynamic surface method and unknown directions can be handled by invoking error compensation mechanism and Nussbaum‐type functions, respectively. The uniqueness of the presented control scheme is that the tracking system can achieve the fixed‐time stability without relying on the boundedness of dead‐zone parameters. The fixed‐time convergence of the output tracking error and the semiglobally fixed‐time stable of closed‐loop system are assured via the developed adaptive fixed‐time command filtered controller. Finally, a practical example is supplied to further validate the availability of the presented theoretic result.

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Command filter-based adaptive fuzzy decentralized control for large-scale nonlinear systems

Cited by 20 publications

References 50 publications

Adaptive fault‐tolerant decentralized tracking control of switched stochastic uncertain nonlinear systems with time‐varying delay

Adaptive fault‐tolerant decentralized tracking control of switched stochastic uncertain nonlinear systems with time‐varying delay

Fuzzy finite‐time adaptive control of switched nonlinear systems with input nonlinearities

Adaptive neural practical fixed‐time command filtered control for multi‐input and multi‐output nonlinear systems with dead zones input and unknown control direction

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