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

Imam Sutrisno; Mohammad Abu Jami'In; Jinglu Hu

doi:10.1109/MIC.2013.6758071

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

Imam Sutrisno, Mohammad Abu Jami'In, Jinglu Hu

Graduate School of Information, Production and Systems

Research output: Contribution to conference › Paper › peer-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 language	English
Pages	762-766
Number of pages	5
DOIs	https://doi.org/10.1109/MIC.2013.6758071
Publication status	Published - 2013 Jan 1
Event	2013 2nd International Conference on Measurement, Information and Control, ICMIC 2013 - Harbin, China Duration: 2013 Aug 16 → 2013 Aug 18

Conference

Conference	2013 2nd International Conference on Measurement, Information and Control, ICMIC 2013
Country/Territory	China
City	Harbin
Period	13/8/16 → 13/8/18

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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10.1109/MIC.2013.6758071

Cite this

Implementation of Lyapunov learning algorithm for fuzzy switching adaptive controller modeled under Quasi-ARX Neural Network. / Sutrisno, Imam; Jami'In, Mohammad Abu; Hu, Jinglu.
2013. 762-766 Paper presented at 2013 2nd International Conference on Measurement, Information and Control, ICMIC 2013, Harbin, China.

Research output: Contribution to conference › Paper › peer-review

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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.",

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author = "Imam Sutrisno and Jami'In, {Mohammad Abu} and Jinglu Hu",

year = "2013",

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doi = "10.1109/MIC.2013.6758071",

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N2 - 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.

AB - 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.

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

Abstract

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Keywords

ASJC Scopus subject areas

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