Neural network-based decoupled sliding mode controller design for discrete-time nonlinear MIMO systems by SPSA algorithm

Ching Hung Lee*, Hao Yuan Hsueh, Jen Chieh Chien

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, a neural network-based sliding-mode controller design approach with decoupled method is proposed for a class of nonlinear discrete-time uncertain multi-input-multi-output (MIMO) systems. The neural network is used to generate the proper control inputs by simultaneous perturbation stochastic approximation (SPSA) algorithm. The decoupled method simplifies the design complexity to achieve asymptotic stability for the uncertain nonlinear system with external disturbance. The proposed control scheme does not need the exactly system model to avoid the mathematical derivation. In addition, the frictional force analysis for the nonlinear inverted double pendulum system is considered to investigate the relationship between controller and frictional force. Simulation results are presented to illustrate the effectiveness of our approach.

Original languageEnglish
Title of host publicationProceedings of the International MultiConference of Engineers and Computer Scientists 2010, IMECS 2010
Pages65-70
Number of pages6
StatePublished - 2010
EventInternational MultiConference of Engineers and Computer Scientists 2010, IMECS 2010 - Kowloon, Hong Kong
Duration: 17 Mar 201019 Mar 2010

Publication series

NameProceedings of the International MultiConference of Engineers and Computer Scientists 2010, IMECS 2010

Conference

ConferenceInternational MultiConference of Engineers and Computer Scientists 2010, IMECS 2010
CountryHong Kong
CityKowloon
Period17/03/1019/03/10

Keywords

  • Decouple
  • Neural network system
  • Nonlinear control
  • Simultaneous perturbation stochastic approximation
  • Sliding mode control

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