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GPGPU-accelerated nonlinear state estimators : application to MPC-controlled bioreactor performance
Dissertation (MEng (Control Engineering))--University of Pretoria, 2021.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2021
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| _version_ | 1867613694501847040 |
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| access_status_str | Open Access |
| author2 | Sandrock, Carl |
| author_browse | Sandrock, Carl |
| author_facet | Sandrock, Carl |
| collection | Thesis |
| dc_rights_str_mv | © 2019 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 (Control Engineering))--University of Pretoria, 2021. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/80969 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:40:13.301Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| 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:2263/80969 GPGPU-accelerated nonlinear state estimators : application to MPC-controlled bioreactor performance Sandrock, Carl u15041604@tuks.co.za De Villiers, Johan Pieter Iplik, Esin Roos, Darren Craig State estimation GPGPU acceleration Gaussian sum filter Particle filter Numba/CuPy UCTD Engineering, built environment and information technology theses SDG-04 Engineering, built environment and information technology theses SDG-08 Engineering, built environment and information technology theses SDG-09 Engineering, built environment and information technology theses SDG-12 Dissertation (MEng (Control Engineering))--University of Pretoria, 2021. Practical control problems are subject to dealing with instrumentation noise and inaccurate models. These can be modelled as measurement and state noise, respectively. Nonlinear state estimators, for example a particle filter, can be used to mitigate these effects. However, they are usually computationally expensive which makes them impractical for industrial use. This text investigates using General Purpose Graphics Processing Units (GPGPU) to improve the performance particle and Gaussian sum filters by parallelizing their prediction, update and resampling steps. GPGPU accelerated filters are found to outperform non-accelerated filters as the number of particle increases. GPGPU acceleration also allows particle filters with 2^19.5 particles to be used on systems with dynamic time constants on the order of 0.1 second and for Gaussian sum filters with 2^18.5 particles to be used with time constants on the order of 1 second. The filters are applied to a bioreactor system containing R. Oryzae, where MPC control is applied to the production phase fumaric acid and glucose concentrations. The bioreactor is modelled using results from Iplik (2017) and Swart (2019). It is found that the GPGPU filters improved run times allow for more particles to be used which provides increased filter accuracy and thus better performance. This improved performance comes at the cost of consuming more energy. Thus, it is believed that the GPGPU implementations should be used for applications with complex dynamics/noise that require large numbers of particles and/or high sampling rates. mi2026 Chemical Engineering MEng (Control Engineering) Unrestricted SDG-04: Quality education SDG-08: Decent work and economic growth SDG-09: Industry, innovation and infrastructure SDG-12: Responsible consumption and production 2021-07-23T12:40:52Z 2021-07-23T12:40:52Z 2021 2021 Dissertation * S2021 http://hdl.handle.net/2263/80969 en © 2019 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 | State estimation GPGPU acceleration Gaussian sum filter Particle filter Numba/CuPy UCTD Engineering, built environment and information technology theses SDG-04 Engineering, built environment and information technology theses SDG-08 Engineering, built environment and information technology theses SDG-09 Engineering, built environment and information technology theses SDG-12 GPGPU-accelerated nonlinear state estimators : application to MPC-controlled bioreactor performance |
| title | GPGPU-accelerated nonlinear state estimators : application to MPC-controlled bioreactor performance |
| title_full | GPGPU-accelerated nonlinear state estimators : application to MPC-controlled bioreactor performance |
| title_fullStr | GPGPU-accelerated nonlinear state estimators : application to MPC-controlled bioreactor performance |
| title_full_unstemmed | GPGPU-accelerated nonlinear state estimators : application to MPC-controlled bioreactor performance |
| title_short | GPGPU-accelerated nonlinear state estimators : application to MPC-controlled bioreactor performance |
| title_sort | gpgpu accelerated nonlinear state estimators application to mpc controlled bioreactor performance |
| topic | State estimation GPGPU acceleration Gaussian sum filter Particle filter Numba/CuPy UCTD Engineering, built environment and information technology theses SDG-04 Engineering, built environment and information technology theses SDG-08 Engineering, built environment and information technology theses SDG-09 Engineering, built environment and information technology theses SDG-12 |
| url | http://hdl.handle.net/2263/80969 |