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- W2990913504 abstract "Abstract This paper is dedicated to neural networks-based adaptive finite-time control design of switched nonlinear systems in the time-varying domain. More specifically, by employing the approximation ability of neural networks system, an integrated adaptive controller is constructed. The main aim is to make sure the closed-loop system in arbitrary switching signals is semi-global practical finite-time stable (SGPFS). A backstepping design with a common Lyapunov function is proposed. Unlike some existing control schemes with actuator failures, the key is dealing with the time-varying fault-tolerant job for the switched system. It is also proved that all signals in the system are bounded and the tracking error can converge in a small field of the origin in finite time. A practical example is presented to illustrate the validity of the theory." @default.
- W2990913504 created "2019-12-05" @default.
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- W2990913504 date "2019-11-21" @default.
- W2990913504 modified "2023-09-23" @default.
- W2990913504 title "Neural networks-based adaptive finite-time control of switched nonlinear systems under time-varying actuator failures" @default.
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- W2990913504 doi "https://doi.org/10.1186/s13662-019-2396-6" @default.
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