Adaptive Neural Network Control Scheme of Switched Systems with Input Saturation

Xiaoli Jiang, Mingyue Liu, Siqi Liu, Jing Xu and Lina Liu

Discrete Dynamics in Nature and Society, 2020, vol. 2020, 1-12

Abstract:

This paper investigates a scheme of adaptive neural network control for a stochastic switched system with input saturation. The unknown smooth nonlinear functions are approximated directly by neural networks. A modified approach is proposed to deal with unknown functions with nonstrict feedback form in the design process. Furthermore, by combining the auxiliary design signal and the adaptive backstepping design, a valid adaptive neural tracking controller design algorithm is presented such that all the signals of the switched closed-loop system are in probability semiglobally, uniformly, and ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in probability. In the end, the effectiveness of the proposed method is verified by a simulation example.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:7259613

DOI: 10.1155/2020/7259613

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