Adaptive discontinuous control for homogeneous systems approximated by neural networks

Mariana Ballesteros, Andrey Polyakov, Denis Efimov, Isaac Chairez, Alexander Poznyak

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Resumen

This study is devoted to the design of an adaptive discontinuous control based on differential neural networks (DNNs) for a class of uncertain homogeneous systems. The control is based on the universal approximation properties of artificial neural networks (ANNs) applied on a certain class of homogeneous nonlinear functions. The adaptation laws for the DNNs parameters are obtained with the application of the Lyapunov stability theory and the homogeneity properties of the approximated nonlinear system. The stability analysis of the closed loop system with the proposed controller is presented. The estimation error in the approximation of the uncertain homogeneous functions is considered in the stability analysis. The performance of the controller is illustrated by means of a numerical simulation of a homogeneous model.

Idioma originalInglés
Páginas (desde-hasta)7885-7890
Número de páginas6
PublicaciónIFAC-PapersOnLine
Volumen53
DOI
EstadoPublicada - 2020
Evento21st IFAC World Congress 2020 - Berlin, Alemania
Duración: 12 jul 202017 jul 2020

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