Fixed-time adaptive neural tracking control for high-order nonlinear switched systems with input saturation and dead-zone

Huanqing Wang and Zhu Meng

Applied Mathematics and Computation, 2024, vol. 480, issue C

Abstract: This article investigates the problem of tracking control for high-order switched nonlinear systems with input saturation and dead-zone. The uncertain nonlinear functions are estimated via applying radial basis function neural networks (RBF NNs). An improved transformation approach is designed to simplify the design complexity caused by input nonlinearities. In particular, a novel filter is presented to handle the difficulty of “explosion of complexity”. With the support of the common Lyapunov function (CLF) approach, a novel neural fixed-time dynamic surface control (DSC) scheme is proposed to assure all the signals in closed-loop systems are bounded and the output signal tracks the expected signal within fixed time. The simulation example illustrates the validity of the proposed control algorithm.

Keywords: Adaptive control; Input nonlinearities; High-order nonlinear switched systems; Fixed-time filter (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:480:y:2024:i:c:s0096300324003655

DOI: 10.1016/j.amc.2024.128904

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