Adaptive output feedback control of uncertain multi-input multi-output systems using single hidden layer neural networks

Naira Hovakimyan, Anthony J. Calise

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We consider adaptive output feedback control of uncertain multi-input multi-output nonlinear systems, in which both the dynamics and the dimension of the regulated plant may be unknown, but knowledge of vector relative degree is required. Given smooth reference trajectories, the problem is to design controllers that force the system measurements to track them with bounded errors. We propose a linear observer for the output tracking error vector and a Single Hidden Layer (SHL) Neural Network (NN) to cancel the modelling errors. Ultimate boundedness of the error signals is shown through Lyapunov's direct method. Simulations of a fourth order two-input two-output nonlinear system illustrate the theoretical results.

Original languageEnglish (US)
Title of host publicationProceedings of the American Control Conference
Pages1555-1560
Number of pages6
DOIs
StatePublished - 2002
Externally publishedYes
Event2002 American Control Conference - Anchorage, AK, United States
Duration: May 8 2002May 10 2002

Publication series

NameProceedings of the American Control Conference
Volume2
ISSN (Print)0743-1619

Other

Other2002 American Control Conference
CountryUnited States
CityAnchorage, AK
Period5/8/025/10/02

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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    Hovakimyan, N., & Calise, A. J. (2002). Adaptive output feedback control of uncertain multi-input multi-output systems using single hidden layer neural networks. In Proceedings of the American Control Conference (pp. 1555-1560). (Proceedings of the American Control Conference; Vol. 2). https://doi.org/10.1109/ACC.2002.1023243