Direct adaptive control via decomposed fuzzy petri net

Shun Feng Su; Ming Chang Chen; Yi Hsing Chien; Wei Yen Wang; Kuo Kai Shyu

doi:10.1109/SMC.2014.6974535

Direct adaptive control via decomposed fuzzy petri net

Shun Feng Su, Ming Chang Chen, Yi Hsing Chien, Wei Yen Wang, Kuo Kai Shyu

Department of Electrical Engineering

Research output: Contribution to journal › Conference article › peer-review

Abstract

This paper presents a novel direct adaptive controller design via decomposed fuzzy Petri net to solve the control tracking problem. The controller combines decomposed fuzzy system (DFS) and Petri net to achieve good performance with less computation time. In the DFS structure, fuzzy variables are decomposed into several layers. DFS has been shown to have fast learning capability but with a complicated system structure. In this study, Petri net is employed to form a mechanism in constructing meaningful component fuzzy systems in the DFS so that the number of fuzzy components can be dramatically reduced without significantly degrading the modeling performance. Finally, the effectiveness of the proposed controller scheme is verified by simulation results.

Original language	English
Article number	6974535
Pages (from-to)	3873-3877
Number of pages	5
Journal	Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume	2014-January
Issue number	January
DOIs	https://doi.org/10.1109/SMC.2014.6974535
Publication status	Published - 2014
Event	2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014 - San Diego, United States Duration: 2014 Oct 5 → 2014 Oct 8

Keywords

Adaptive control
Decomposed fuzzy system
Fuzzy petri net

ASJC Scopus subject areas

Electrical and Electronic Engineering
Control and Systems Engineering
Human-Computer Interaction

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title = "Direct adaptive control via decomposed fuzzy petri net",

abstract = "This paper presents a novel direct adaptive controller design via decomposed fuzzy Petri net to solve the control tracking problem. The controller combines decomposed fuzzy system (DFS) and Petri net to achieve good performance with less computation time. In the DFS structure, fuzzy variables are decomposed into several layers. DFS has been shown to have fast learning capability but with a complicated system structure. In this study, Petri net is employed to form a mechanism in constructing meaningful component fuzzy systems in the DFS so that the number of fuzzy components can be dramatically reduced without significantly degrading the modeling performance. Finally, the effectiveness of the proposed controller scheme is verified by simulation results.",

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AU - Su, Shun Feng

AU - Chen, Ming Chang

AU - Chien, Yi Hsing

AU - Wang, Wei Yen

AU - Shyu, Kuo Kai

PY - 2014

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N2 - This paper presents a novel direct adaptive controller design via decomposed fuzzy Petri net to solve the control tracking problem. The controller combines decomposed fuzzy system (DFS) and Petri net to achieve good performance with less computation time. In the DFS structure, fuzzy variables are decomposed into several layers. DFS has been shown to have fast learning capability but with a complicated system structure. In this study, Petri net is employed to form a mechanism in constructing meaningful component fuzzy systems in the DFS so that the number of fuzzy components can be dramatically reduced without significantly degrading the modeling performance. Finally, the effectiveness of the proposed controller scheme is verified by simulation results.

AB - This paper presents a novel direct adaptive controller design via decomposed fuzzy Petri net to solve the control tracking problem. The controller combines decomposed fuzzy system (DFS) and Petri net to achieve good performance with less computation time. In the DFS structure, fuzzy variables are decomposed into several layers. DFS has been shown to have fast learning capability but with a complicated system structure. In this study, Petri net is employed to form a mechanism in constructing meaningful component fuzzy systems in the DFS so that the number of fuzzy components can be dramatically reduced without significantly degrading the modeling performance. Finally, the effectiveness of the proposed controller scheme is verified by simulation results.

KW - Adaptive control

KW - Decomposed fuzzy system

KW - Fuzzy petri net

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