Rule-bases construction through self-learning for a table-based Sugeno-Takagi fuzzy logic control system
A self-learning based methodology for building the rule-base of a fuzzy logic controller (FLC) is presented and verified, aiming to engage intelligent characteristics to a fuzzy logic control systems. The methodology is a simplified version of those presented in today literature. Some aspects are in...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Editura Universităţii "Petru Maior"
2009-12-01
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| Series: | Scientific Bulletin of the ''Petru Maior" University of Tîrgu Mureș |
| Subjects: | |
| Online Access: | http://scientificbulletin.upm.ro/papers/2009/10/Rule-bases-construction-through-self-learning-for-a-table-based-Sugeno-Takagi-fuzzy-logic-control-system.pdf |
| Summary: | A self-learning based methodology for building the rule-base of a fuzzy logic controller (FLC) is presented and verified, aiming to engage intelligent characteristics to a fuzzy logic control systems. The methodology is a simplified version of those presented in today literature. Some aspects are intentionally ignored since it rarely appears in control system engineering and a SISO process is considered here. The fuzzy inference system obtained is a table-based Sugeno-Takagi type. System’s desired performance is defined by a reference model and rules are extracted from recorded data, after the correct control actions are learned. The presented algorithm is tested in constructing the rule-base of a fuzzy controller for a DC drive application. System’s performances and method’s viability are analyzed. |
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| ISSN: | 1841-9267 2285-438X |