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Robust neural control of discrete time uncertain nonlinear systems using sliding mode backpropagation training algorithm

  • Imen ZaidiEmail author
  • Mohamed Chtourou
  • Mohamed Djemel
Research Article

Abstract

This work deals with robust inverse neural control strategy for a class of single-input single-output (SISO) discrete-time nonlinear system affected by parametric uncertainties. According to the control scheme, in the first step, a direct neural model (DNM) is used to learn the behavior of the system, then, an inverse neural model (INM) is synthesized using a specialized learning technique and cascaded to the uncertain system as a controller. In previous works, the neural models are trained classically by backpropagation (BP) algorithm. In this work, the sliding mode-backpropagation (SM-BP) algorithm, presenting some important properties such as robustness and speedy learning, is investigated. Moreover, four combinations using classical BP and SM-BP are tested to determine the best configuration for the robust control of uncertain nonlinear systems. Two simulation examples are treated to illustrate the effectiveness of the proposed control strategy.

Keywords

Discrete time uncertain nonlinear systems neural modelling sliding mode backpropagation (BP) algorithm robust neural control 

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Copyright information

© Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Control & Energy Management Laboratory, National School of Sfax EngineersUniversity of SfaxSfaxTunisia

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