Implementation of Lyapunov learning algorithm for fuzzy switching adaptive controller modeled under Quasi-ARX Neural Network

Imam Sutrisno, Mohammad Abu Jami'In, Jinglu Hu

Research output: Contribution to conferencePaperpeer-review

Abstract

This paper presents a fuzzy adaptive controller applied to a non linear system modeled under a Quasi-linear ARX Neural Network, with stability proof by using the Lyapunov approach. This work exploits the new idea to use Lyapunov function to train multi-input multi-output neural network on the core-part sub-model. The proposed controller is designed between a non linear controller and linear controller based on fuzzy switching algorithm. Finally improving performances of the Lyapunov learning algorithm are stable in the learning process, fast convergence of error, and able to increase the accuracy of the controller.

Original languageEnglish
Pages762-766
Number of pages5
DOIs
Publication statusPublished - 2013 Jan 1
Event2013 2nd International Conference on Measurement, Information and Control, ICMIC 2013 - Harbin, China
Duration: 2013 Aug 162013 Aug 18

Conference

Conference2013 2nd International Conference on Measurement, Information and Control, ICMIC 2013
CountryChina
CityHarbin
Period13/8/1613/8/18

Keywords

  • Fuzzy Switching Adaptive Controller
  • Lyapunov Learning Algorithm
  • Quasi-ARX Neural Network

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Education

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