Adaptive neural networks control for MIMO nonlinear systems with unmeasured states and unmodeled dynamics

Sanxia Wang, Jianwei Xia, Xueliang Wang, Wenjing Yang and Linqi Wang

Applied Mathematics and Computation, 2021, vol. 408, issue C

Abstract: In this paper, an adaptive neural networks control scheme is developed for a class of multi input and multi output uncertain nonlinear systems with unmeasured states and unmodeled dynamics. In the control scheme, a dynamic signal is used to deal with the unmodeled dynamics and a neural observer is designed to estimate the unmeasured states. Meanwhile, the neural networks are used to estimate the combinational unknown nonlinear function at each step of backstepping process. It is proved that all signals of the closed-loop system are semi global uniformly ultimately bounded (SGUUB). Finally, a simulation example is provided to show the effectiveness of the proposed control method.

Keywords: Adaptive neural networks control; Multi input and multi output uncertain nonlinear systems; Unmeasured states; Unmodeled dynamics (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:408:y:2021:i:c:s0096300321004586

DOI: 10.1016/j.amc.2021.126369

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