Adaptive output feedback control of a class of non-linear systems using neural networks

Naira Hovakimyan, Flavio Nardi, Anthony J. Calise, Hungu Lee

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents tools for the design of a neural network based adaptive output feedback controller for a class of partially or completely unknown non-linear multi-input multi-output systems without zero dynamics. Each of the outputs is assumed to have relative degree less or equal to 2. A neural network based adaptive observer is designed to estimate the derivatives of the outputs. Subsequently, the adaptive observer is integrated into a neural network based adaptive controller architecture. Conditions are derived which guarantee the ultimate boundedness of all the errors in the closed loop system. Stability analysis reveals simultaneous learning rules for both the adaptive neural network observer and adaptive neural network controller. The design approach is illustrated using a fourth order two-input two-output example, in which each output has relative degree two.

Original languageEnglish (US)
Pages (from-to)1161-1169
Number of pages9
JournalInternational Journal of Control
Volume74
Issue number12
DOIs
StatePublished - Aug 15 2001
Externally publishedYes

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications

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