A disturbance reduction scheme for linear small delay systems with modeling uncertainties

Ming Hau Tsai, Pi Cheng Tung

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

A disturbance reduction scheme for linear delay systems with modeling uncertainties is presented in this paper. The linear systems in this study are assumed to be nominally stable, minimum phase and relative degree one systems. The control structure is based on Astrom's modified Smith predictor with a disturbance reduction scheme and an artificial neural network (ANN). Unlike other disturbance rejection methods, the proposed scheme does not require information about unknown disturbance frequencies. The ANN is used to approximate a product of an inverse of a time delay and a nonnegative gain and to augment the robustness of the proposed approach against modeling uncertainties including a time-varying delay. Connective weights of the ANN are trained on-line using a back-propagation algorithm according to a disturbance estimation error function. Simulation results show the effectiveness of the presented disturbance reduction scheme for linear delay systems with modeling uncertainties, subjected to both periodic and non-periodic unknown load disturbances.

Original languageEnglish
Pages (from-to)777-786
Number of pages10
JournalJournal of Process Control
Volume20
Issue number6
DOIs
StatePublished - Jul 2010

Keywords

  • Artificial neural network
  • Disturbance reduction
  • Modified Smith predictor
  • Time delay

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