State-space self-tuning control for nonlinear stochastic and chaotic hybrid systems

Shu Mei Guo, Leang San Shieh, Ching Fang Lin, Jagdish Chandra

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)

Abstract

This paper presents a new state-space self-tuning control scheme for adaptive digital control of continuous-time multivariable nonlinear stochastic and chaotic systems, which have unknown system parameters, system and measurement noises, and inaccessible system states. Instead of using the moving average (MA)-based noise model commonly used for adaptive digital control of linear discrete-time stochastic systems in the literature, an adjustable auto-regressive moving average (ARMA)-based noise model with estimated states is constructed for state-space self-tuning control of nonlinear continuous-time stochastic systems. By taking advantage of a digital redesign methodology, which converts a predesigned high-gain analog tracker/observer into a practically implementable low-gain digital tracker/observer, and by taking the non-negligible computation time delay and a relatively longer sampling period into consideration, a digitally redesigned predictive tracker/observer has been newly developed in this paper for adaptive chaotic orbit tracking. The proposed method enables the development of a digitally implementable advanced control algorithm for nonlinear stochastic and chaotic hybrid systems.

Original languageEnglish
Pages (from-to)1079-1113
Number of pages35
JournalInternational Journal of Bifurcation and Chaos in Applied Sciences and Engineering
Volume11
Issue number4
DOIs
Publication statusPublished - 2001 Apr

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • Engineering (miscellaneous)
  • General
  • Applied Mathematics

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