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 | |
| Publication status | Published - 2014 Jan 1 |
| Event | 2014 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2014 - San Diego, United States Duration: 2014 Oct 5 → 2014 Oct 8 |
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Keywords
- Adaptive control
- Decomposed fuzzy system
- Fuzzy petri net
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Human-Computer Interaction
Cite this
Direct adaptive control via decomposed fuzzy petri net. / Su, Shun Feng; Chen, Ming Chang; Chien, Yi Hsing; Wang, Wei-Yen; Shyu, Kuo Kai.
In: Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, Vol. 2014-January, No. January, 6974535, 01.01.2014, p. 3873-3877.Research output: Contribution to journal › Conference article
}
TY - JOUR
T1 - Direct adaptive control via decomposed fuzzy petri net
AU - Su, Shun Feng
AU - Chen, Ming Chang
AU - Chien, Yi Hsing
AU - Wang, Wei-Yen
AU - Shyu, Kuo Kai
PY - 2014/1/1
Y1 - 2014/1/1
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
UR - http://www.scopus.com/inward/record.url?scp=84938125627&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84938125627&partnerID=8YFLogxK
U2 - 10.1109/SMC.2014.6974535
DO - 10.1109/SMC.2014.6974535
M3 - Conference article
AN - SCOPUS:84938125627
VL - 2014-January
SP - 3873
EP - 3877
JO - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
JF - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SN - 1062-922X
IS - January
M1 - 6974535
ER -