Automated robust control system design for variable speed drives
Traditional PI controllers have been largely employed for the control of industrial variable speed drives due to the design ease and performance satisfaction they provide but, the problem is that these controllers do not always provide robust performance under variable loads. Existing solutions pres...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
| Published: |
2008
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| Online Access: | https://eprints.nottingham.ac.uk/10584/ |
| _version_ | 1848791100233351168 |
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| author | Okaeme, Nnamdi |
| author_facet | Okaeme, Nnamdi |
| author_sort | Okaeme, Nnamdi |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Traditional PI controllers have been largely employed for the control of industrial variable speed drives due to the design ease and performance satisfaction they provide but, the problem is that these controllers do not always provide robust performance under variable loads. Existing solutions present themselves as complex control strategies that demand specialist expertise for their implementation. As a direct consequence, these factors have limited their adoption for the industrial control of drives. To counter this trend, the thesis proposes two techniques for robust control system design. The developed strategies employ particular Evolutionary Algorithms EA), which enable their simple and automated implementation. More specifically, the EA employed and tested are the Genetic Algorithms (GA), Bacterial Foraging (BF) and the novel Hybrid Bacterial Foraging, which combines specific desirable features of the GA and BF.
The first technique, aptly termed Robust Experimental Control Design, employs the above mentioned EA in an automated trial-and-error approach that involves directly testing control parameters on the experimental drive system, while it operates under variable mechanical loads, evolving towards the best possible solutions to the control problem. The second strategy, Robust Identification-based Control Design, involves a GA system identification procedure employed in automatically defining an uncertainty model for the variable mechanical loads and, through the adoption of the Frequency Domain H-infinity Method in combination with the developed EA, robust controllers for drive systems are designed. The results that highlight the effectiveness of the robust control system design techniques are presented. Performance comparisons between the design techniques and amongst the employed EA are also shown. The developed techniques possess commercially viable qualities because they elude the need for skilled expertise in their implementation and are deployed in a simple and automated fashion. |
| first_indexed | 2025-11-14T18:23:08Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-10584 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T18:23:08Z |
| publishDate | 2008 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-105842025-02-28T11:08:50Z https://eprints.nottingham.ac.uk/10584/ Automated robust control system design for variable speed drives Okaeme, Nnamdi Traditional PI controllers have been largely employed for the control of industrial variable speed drives due to the design ease and performance satisfaction they provide but, the problem is that these controllers do not always provide robust performance under variable loads. Existing solutions present themselves as complex control strategies that demand specialist expertise for their implementation. As a direct consequence, these factors have limited their adoption for the industrial control of drives. To counter this trend, the thesis proposes two techniques for robust control system design. The developed strategies employ particular Evolutionary Algorithms EA), which enable their simple and automated implementation. More specifically, the EA employed and tested are the Genetic Algorithms (GA), Bacterial Foraging (BF) and the novel Hybrid Bacterial Foraging, which combines specific desirable features of the GA and BF. The first technique, aptly termed Robust Experimental Control Design, employs the above mentioned EA in an automated trial-and-error approach that involves directly testing control parameters on the experimental drive system, while it operates under variable mechanical loads, evolving towards the best possible solutions to the control problem. The second strategy, Robust Identification-based Control Design, involves a GA system identification procedure employed in automatically defining an uncertainty model for the variable mechanical loads and, through the adoption of the Frequency Domain H-infinity Method in combination with the developed EA, robust controllers for drive systems are designed. The results that highlight the effectiveness of the robust control system design techniques are presented. Performance comparisons between the design techniques and amongst the employed EA are also shown. The developed techniques possess commercially viable qualities because they elude the need for skilled expertise in their implementation and are deployed in a simple and automated fashion. 2008 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en arr https://eprints.nottingham.ac.uk/10584/1/Thesis.pdf Okaeme, Nnamdi (2008) Automated robust control system design for variable speed drives. PhD thesis, University of Nottingham. Genetic Algorithms Bacterial Foraging Evolutionary Algorithms Variable Speed Drives Robust Control Systems Programmable Load Emulators |
| spellingShingle | Genetic Algorithms Bacterial Foraging Evolutionary Algorithms Variable Speed Drives Robust Control Systems Programmable Load Emulators Okaeme, Nnamdi Automated robust control system design for variable speed drives |
| title | Automated robust control system design for variable speed drives |
| title_full | Automated robust control system design for variable speed drives |
| title_fullStr | Automated robust control system design for variable speed drives |
| title_full_unstemmed | Automated robust control system design for variable speed drives |
| title_short | Automated robust control system design for variable speed drives |
| title_sort | automated robust control system design for variable speed drives |
| topic | Genetic Algorithms Bacterial Foraging Evolutionary Algorithms Variable Speed Drives Robust Control Systems Programmable Load Emulators |
| url | https://eprints.nottingham.ac.uk/10584/ |