Probability distribution modelling to improve stability in nonlinear MIMO control

Randa Herzallah, David Lowe

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

Abstract

We consider the direct adaptive inverse control of nonlinear multivariable systems with different delays between every input-output pair. In direct adaptive inverse control, the inverse mapping is learned from examples of input-output pairs. This makes the obtained controller sub optimal, since the network may have to learn the response of the plant over a larger operational range than necessary. Moreover, in certain applications, the control problem can be redundant, implying that the inverse problem is ill posed. In this paper we propose a new algorithm which allows estimating and exploiting uncertainty in nonlinear multivariable control systems. This approach allows us to model strongly non-Gaussian distribution of control signals as well as processes with hysteresis. The proposed algorithm circumvents the dynamic programming problem by using the predicted neural network uncertainty to localise the possible control solutions to consider.
Original languageEnglish
Title of host publicationProceedings of 2003 IEEE Conference on Control Applications (CCA)
PublisherIEEE
Pages954-959
Number of pages6
Volume2
ISBN (Print)0-7803-772-9
DOIs
Publication statusPublished - 25 Jun 2003
Event2003 IEEE Conference on Control Applications - Istanbul, Turkey
Duration: 23 Jun 200325 Jun 2003

Conference

Conference2003 IEEE Conference on Control Applications
Abbreviated titleCCA 2003
CountryTurkey
CityIstanbul
Period23/06/0325/06/03

Bibliographical note

©2003 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

Keywords

  • statistical distributions
  • multivariable control systems
  • stability
  • MIMO systems
  • nonlinear control systems
  • hysteresis
  • dynamic programming
  • neural nets
  • probability distribution modelling
  • nonlinear MIMO control
  • direct adaptive inverse con

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  • Cite this

    Herzallah, R., & Lowe, D. (2003). Probability distribution modelling to improve stability in nonlinear MIMO control. In Proceedings of 2003 IEEE Conference on Control Applications (CCA) (Vol. 2, pp. 954-959). IEEE. https://doi.org/10.1109/CCA.2003.1223139