Neural network-based digital redesign approach for control of unknown continuous-time chaotic systems

Jose I. Canelon, Leang S. Shieh, Shu M. Guo, Heidar A. Malki

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents a neural network-based digital redesign approach for digital control of continuous-time chaotic systems with unknown structures and parameters. Important features of the method are that: (i) it generalizes the existing optimal linearization approach for the class of state-space models which are nonlinear in the state but linear in the input, to models which are nonlinear in both the state and the input; (ii) it develops a neural network-based universal optimal linear state-space model for unknown chaotic systems; (iii) it develops an anti-digital redesign approach for indirectly estimating an analog control law from a fast-rate digital control law without utilizing the analog models. The estimated analog control law is then converted to a slow-rate digital control law via the prediction-based digital redesign method; (iv) it develops a linear time-varying piecewise-constant low-gain tracker which can be implemented using micro-processors. Illustrative examples are presented to demonstrate the effectiveness of the proposed methodology.

Original languageEnglish
Pages (from-to)2433-2455
Number of pages23
JournalInternational Journal of Bifurcation and Chaos in Applied Sciences and Engineering
Volume15
Issue number8
DOIs
Publication statusPublished - 2005 Aug

All Science Journal Classification (ASJC) codes

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

Fingerprint Dive into the research topics of 'Neural network-based digital redesign approach for control of unknown continuous-time chaotic systems'. Together they form a unique fingerprint.

Cite this