On-Line Self-Organizing Fuzzy Controllers for Magnetic Levitation Control Systems
碩士 === 崑山科技大學 === 電機工程研究所 === 98 === This thesis proposes a new control approach for magnetic levitation systems using self-organizing fuzzy controllers. The relevant features of the magnetic levitation control systems (MLCSs) are its inherent instability and nonlinearity in electromechanical dy...
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| Format: | Others |
| Language: | zh-TW |
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2010
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| Online Access: | http://ndltd.ncl.edu.tw/handle/44100195760693297718 |
| Summary: | 碩士 === 崑山科技大學 === 電機工程研究所 === 98 === This thesis proposes a new control approach for magnetic levitation systems using self-organizing fuzzy controllers. The relevant features of the magnetic levitation control systems (MLCSs) are its inherent instability and nonlinearity in electromechanical dynamics. Development of a high-performance controller for the position control of a MLCS is thus quite important in applications. The fuzzy controller and the related artificial intelligence (AI) approaches have been successfully applied to the position control of a MLCS. The disadvantage of this sort of method is that design of an optimal fuzzy model for the dynamic system is often difficult. In this thesis, a hybrid particle swarm optimization (HPSO) algorithm is relied on to solve the problem of determining the best fuzzy partition of the input spaces and associated fuzzy membership functions for each input variable. Since HPSO is an excellent optimization tool, it is easily adequate to design the optimal fuzzy control model. The proposed approach has been verified on a microprocessor-based self-designed MLCS. The results show the proposed self-organizing fuzzy controller can provide better position control than existing methods.
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