Finite-time adaptive neural command filtered control for non-strict feedback uncertain multi-agent systems including prescribed performance and input nonlinearities

Ziwen Wu, Tianping Zhang, Xiaonan Xia and Yu Hua

Applied Mathematics and Computation, 2022, vol. 421, issue C

Abstract: In this paper, the issue of finite-time consensus tracking control (CTC) is discussed for uncertain nonlinear multi-agent systems (MASs) with prescribed performance and input saturation. By constructing a finite-time performance function (FTPF), the tracking errors converge to a predefined attenuation range within finite-time. By using tanh(·) function and mathematical transformation, the effect of input saturation is solved. The unmodeled dynamics and dynamic disturbances of the system are solved by means of measurable dynamic signals. Through the Young’s inequality and the properties of Gaussian function, the coupling problem between multi-agents and the system controller design problem in non-strict feedback form are successfully dealt with. Furthermore, a finite-time adaptive neural controller is designed based on command filter, which not only guarantees the finite-time stability of the system, but also makes the tracking errors reach a predefined bound in a finite-time. Stability analysis proves that the proposed method is feasible, and simulation verifies its effectiveness.

Keywords: Multi-agent systems; Prescribed performance; Input nonlinearities; Finite-time control; Command filter; Unmodeled dynamics (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:421:y:2022:i:c:s009630032200039x

DOI: 10.1016/j.amc.2022.126953

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