Control of chaotic dynamical systems using radial basis function network approximators

Keun Bum Kim, Jin Bae Park, Yoon Ho Choi, Guanrong Chen

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)


This paper presents a general control method based on radial basis function networks (RBFNs) for chaotic dynamical systems. For many chaotic systems that can be decomposed into a sum of a linear and a nonlinear part, under some mild conditions the RBFN can be used to well approximate the nonlinear part of the system dynamics. The resulting system is then dominated by the linear part, with some small or weak residual nonlinearities due to the RBFN approximation errors. Thus, a simple linear state-feedback controller can be devised, to drive the system response to a desirable set-point. In addition to some theoretical analysis, computer simulations on two representative continuous-time chaotic systems (the Duffing and the Lorenz systems) are presented to demonstrate the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)165-183
Number of pages19
JournalInformation sciences
Issue number1-4
Publication statusPublished - 2000 Dec

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence


Dive into the research topics of 'Control of chaotic dynamical systems using radial basis function network approximators'. Together they form a unique fingerprint.

Cite this