High order sliding mode neurocontrol for uncertain nonlinear SISO systems:Theory and applications

Isaac Chairez, Alexander Poznyak, Tatyana Poznyak

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

1 Scopus citations


Uncertainties in dynamic systems are common in real applications, provoking substantial troubles in any control realization and being a source of instability or poor performance for tracking or regulation problems. Considerable research efforts had been undertaken on control designing for uncertain nonlinear dynamic systems over the last thirty years. There are several approaches to design and construct a control in this situation. Among them, the more effective are the Artificial Neural Networks (ANN) and the Sliding Mode (SM) technique with all possible variants within (Integral Sliding Mode, Higher Order Sliding Mode, etc.). Such combination seems to be very promising [21], [28] because it provides a new instrument for identification, state estimation and control of many classes of uncertain systems affected by external perturbations. This chapter deals with the realization of this idea and suggests an adaptive control designing based on both Differential Neural Network Observation and High Order Sliding Mode Technique. Below this approach is referred to as High Order Sliding Mode Neural Control (HOSMNC).

Original languageEnglish
Title of host publicationModern Sliding Mode Control Theory
Subtitle of host publicationNew Perspectives and Applications
EditorsGiorgio Bartolini, Alessandro Pisano, Elio Usai, Leonid Fridman
Number of pages22
StatePublished - 2008

Publication series

NameLecture Notes in Control and Information Sciences
ISSN (Print)0170-8643


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