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On-line auto-tuning of multivariable industrial processes using Bayesian optimisation
Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2023.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2023
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| _version_ | 1867613578049093632 |
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| access_status_str | Open Access |
| author2 | Craig, Ian K |
| author_browse | Craig, Ian K |
| author_facet | Craig, Ian K |
| collection | Thesis |
| dc_rights_str_mv | © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2023. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/91252 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:38:22.209Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/91252 On-line auto-tuning of multivariable industrial processes using Bayesian optimisation Craig, Ian K jonathan.vanniekerk@icloud.com Le Roux, Johan Derik Van Niekerk, Jonathan Anson UCTD Bayesian optimisation Gaussian processes Auto-tuning Multi-input multi-output (MIMO) controllers Bulk tailing treatment Engineering, built environment and information technology SDG-09 SDG-09: Industry, innovation and infrastructure Dissertation (MEng (Electronic Engineering))--University of Pretoria, 2023. Auto-tuning of multi-input multi-output (MIMO) controllers of a bulk tailing treatment (BTT) surge tank is presented. Two controllers are selected for optimisation. The first controller is a decentralised proportional-integral (PI) controller that controls a plant simulated using a linear model. The second controller is a multivariable inverse PI controller that controls a plant simulated using a non-linear process model. Objective functions are designed to promote set point tracking and disturbance rejection. The search domain constraints are determined by intuitively expanding the search domain around the tuning parameters of the reference controller. Results show that Bayesian optimisation is successful in improving the performance of the set point tracking and disturbance rejection controllers for the surge tank process. Electrical, Electronic and Computer Engineering MEng (Electronic Engineering) Unrestricted 2023-07-03T12:35:23Z 2023-07-03T12:35:23Z 2023 2023 Dissertation Van Niekerk, 2023, On-line auto-tuning of multivariable industrial processes using Bayesian optimisation, Dissertation, University of Pretoria, Pretoria. http://hdl.handle.net/2263/91252 DOI:https://doi.org/10.25403/UPresearchdata.23554806.v1 https://doi.org/10.25403/UPresearchdata.23554806.v1 en © 2023 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Bayesian optimisation Gaussian processes Auto-tuning Multi-input multi-output (MIMO) controllers Bulk tailing treatment Engineering, built environment and information technology SDG-09 SDG-09: Industry, innovation and infrastructure On-line auto-tuning of multivariable industrial processes using Bayesian optimisation |
| title | On-line auto-tuning of multivariable industrial processes using Bayesian optimisation |
| title_full | On-line auto-tuning of multivariable industrial processes using Bayesian optimisation |
| title_fullStr | On-line auto-tuning of multivariable industrial processes using Bayesian optimisation |
| title_full_unstemmed | On-line auto-tuning of multivariable industrial processes using Bayesian optimisation |
| title_short | On-line auto-tuning of multivariable industrial processes using Bayesian optimisation |
| title_sort | on line auto tuning of multivariable industrial processes using bayesian optimisation |
| topic | UCTD Bayesian optimisation Gaussian processes Auto-tuning Multi-input multi-output (MIMO) controllers Bulk tailing treatment Engineering, built environment and information technology SDG-09 SDG-09: Industry, innovation and infrastructure |
| url | http://hdl.handle.net/2263/91252 https://doi.org/10.25403/UPresearchdata.23554806.v1 |