Terminal sliding mode control of nonlinear chaotic systems using self-recurrent wavelet neural network

Sin Ho Lee, Jin Bae Park, Yoon Ho Choi

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

10 Citations (Scopus)

Abstract

In this paper, we design a self-recurrent wavelet neural network (SRWNN) based terminal sliding mode controller for nonlinear chaotic systems with uncertainties. The nonlinear chaotic systems are decomposed into a sum of a nominal nonlinear part and uncertainty term. The terminal sliding mode control (TSMC) method which has been used to control the system robustly can drive the tracking errors to zero in a finite time. In addition, the TSMC has the advantages such as improved performance, robustness, reliability and precision by contrast with the classical sliding mode control (CSMC). For the control of nonlinear chaotic system with various uncertainties, we employ the SRWNN which is used for the prediction of uncertainties. The weights of SRWNN are trained by adaptive laws based on Lyapunov stability theorem. Finally, we carry out simulations on two nonlinear chaotic systems such as Duffing system and Lorenz system to illustrate the effectiveness of the proposed control.

Original languageEnglish
Title of host publicationICCAS 2007 - International Conference on Control, Automation and Systems
Pages1671-1676
Number of pages6
DOIs
Publication statusPublished - 2007 Dec 1
EventInternational Conference on Control, Automation and Systems, ICCAS 2007 - Seoul, Korea, Republic of
Duration: 2007 Oct 172007 Oct 20

Publication series

NameICCAS 2007 - International Conference on Control, Automation and Systems

Other

OtherInternational Conference on Control, Automation and Systems, ICCAS 2007
CountryKorea, Republic of
CitySeoul
Period07/10/1707/10/20

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Terminal sliding mode control of nonlinear chaotic systems using self-recurrent wavelet neural network'. Together they form a unique fingerprint.

  • Cite this

    Lee, S. H., Park, J. B., & Choi, Y. H. (2007). Terminal sliding mode control of nonlinear chaotic systems using self-recurrent wavelet neural network. In ICCAS 2007 - International Conference on Control, Automation and Systems (pp. 1671-1676). [4406603] (ICCAS 2007 - International Conference on Control, Automation and Systems). https://doi.org/10.1109/ICCAS.2007.4406603