Adaptive Discontinuous Control for Homogeneous Systems Approximated by Neural Networks⁎This work was partially supported by the Government of Russian Federation (Grant 08-08) and the Ministry of Education and Science of Russian Federation (Project 14.Z50.31.0031). Issue 2 (2020)

• Record Type: Journal Article
• Authors: Ballesteros, Mariana; Polyakov, Andrey; Efimov, Denis; Chairez, Isaac; Poznyak, Alexander
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 7885
• Page End: 7890
• Publication Date: 2020
• Subjects: Homogeneous systems -- Neural Networks -- Adaptive Control -- Discontinuous Control -- System Identification; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.1971
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: 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.