Adaptive neural network controller design for a class of nonlinear systems using SPSA algorithm

Ching Hung Lee*, Tsung Min Yu, Jen Chieh Chien

*Corresponding author for this work

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

6 Scopus citations

Abstract

In this paper, we propose a novel SPSA-based on-line adaptive decoupled control scheme by using PID neural network for a class of nonlinear systems. In addition, the update laws of parameters with adaptive optimal learning rate are proposed based on the Lyapunov stability theorem, this guarantees the stability of closed-loop system. In addition, the affect of the frictional force model and uncertainty are discussed and analyzes. The proposed approach is applied in the translational oscillations with a rotational actuator (TORA) system. In experimental results, the proposed control is realized by DSP to demonstrate the performance and the efficiency.

Original languageEnglish
Title of host publicationIMECS 2011 - International MultiConference of Engineers and Computer Scientists 2011
Pages868-873
Number of pages6
StatePublished - 2011
EventInternational MultiConference of Engineers and Computer Scientists 2011, IMECS 2011 - Kowloon, Hong Kong
Duration: 16 Mar 201118 Mar 2011

Publication series

NameIMECS 2011 - International MultiConference of Engineers and Computer Scientists 2011
Volume2

Conference

ConferenceInternational MultiConference of Engineers and Computer Scientists 2011, IMECS 2011
Country/TerritoryHong Kong
CityKowloon
Period16/03/1118/03/11

Keywords

  • Adaptive control
  • PID neural network
  • Real-time control
  • Simultaneous perturbation stochastic approximation

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