2022
DOI: 10.1002/acs.3546
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Composite adaptive finite‐time fuzzy control for switched nonlinear systems with preassigned performance

Abstract: This article investigates the composite adaptive fuzzy finite-time prescribed performance control issue of switched nonlinear systems subject to the unknown external disturbance and performance requirement. First, by utilizing the compensation and prediction errors, the piecewise switched composite parameter update law is employed to improve the approximation accuracy of the unknown nonlinearity. Then, the improved fractional-order filter and error compensation signal are introduced to cope with the influences… Show more

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Cited by 58 publications
(3 citation statements)
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“…where X denotes the Fourier transform. According to (9), it is obvious that τU ≤ 0 holds for all solutions of ( 16), which is equivalent to (21). Note that Q H f ≥ 0; then, we have tr(U Q H f ) ≤ 0.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…where X denotes the Fourier transform. According to (9), it is obvious that τU ≤ 0 holds for all solutions of ( 16), which is equivalent to (21). Note that Q H f ≥ 0; then, we have tr(U Q H f ) ≤ 0.…”
Section: Resultsmentioning
confidence: 99%
“…The nonlinear dynamics and negative incremental impedance of CPLs may contribute to the deterioration of system performance and even system paralysis. To address this, scholars have explored various methods, among which the T-S fuzzy method can effectively approximate nonlinearities by a convex sum of local linear systems [6,7], facilitating quantities of interesting research on CPLs of DC microgrids [8,9]. Regional nonlinear methods are also used for system stability analysis [10,11].…”
Section: Introductionmentioning
confidence: 99%
“…In many past researches, although good control or synchronization effects [10], [11], have been made on chaotic systems, they have been studied in an ideal state, and the unknown functions and random noise have not been really taken into account in the system. Some methods can be used to solve these problems, such as the use of neural network methods or fuzzy methods [12] [13] to approximate unknown functions in the system, and the use of disturbance observer [14] [15] to deal with random noise.…”
Section: Introductionmentioning
confidence: 99%