Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Multi-disciplinary Studies | en_US |
| dc.creator | Chan, Ping-tong | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/2468 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | - |
| dc.rights | All rights reserved | en_US |
| dc.title | Optimising fuzzy logic controller | en_US |
| dcterms.abstract | An optimal fuzzy logic controller tuning algorithm is being developed in this project. With the aid of genetic algorithms, optimal rules of fuzzy logic controllers can be designed with little human experience and/or control engineers' knowledge. This is to be solved by deriving a tailor-made encode scheme, initialisation, crossover and mutation of rule table into chromosomes. This project proposes modifications of genetic algorithms to suit the optimisation of fuzzy controller. The genetic algorithm should incorporate as much existing knowledge of the system as possible to increase the speed of optimisation. The basic structure of the controller is outlined and the designed problems associated with the conventional trail and error schemes are addressed. The suitability of the genetic algorithms optimisation techniques as a means to determine and optimise the fuzzy logic controller design is discussed. The proposed algorithm was then applied to linear and non-linear system. In the linear system, the proposed algorithm showed a significant improvement in convergence. The report included a scientific analysis. The non-linear system test the truck backing up problems. The proposed algorithm first solve the problem for single test point and multi-test points. Next, it find out the solution for generalisation of the whole plane. Finally, it provided a shortest distance solution for the whole plane. The problems are repeated for a more complicated truck-and-trail. The proposed algorithm also showed good performance. The results conclude the effective and efficient of the proposed algorithm which applied to different system and can accommodate different performance criterions. | en_US |
| dcterms.extent | [86] leaves : ill. ; 30 cm | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 1996 | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.LCSH | Automatic control | en_US |
| dcterms.LCSH | Fuzzy logic | en_US |
| dcterms.LCSH | Genetic algorithms | en_US |
| dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| b12426271.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 2.48 MB | Adobe PDF | View/Open |
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