Parallelized Model Predictive Control
Model predictive control (MPC) has been used in many industrial applications because of its ability to produce optimal performance while accommodating constraints. However, its application on plants with fast time constants is difficult because of its computationally expensive algorithm. In this res...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute of Electrical and Electronics Engineers (IEEE)
2014
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| Online Access: | http://hdl.handle.net/1721.1/86999 https://orcid.org/0000-0002-4354-0459 |
| _version_ | 1826195237233491968 |
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| author | Soudbakhsh, Damoon Annaswamy, Anuradha M. |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Soudbakhsh, Damoon Annaswamy, Anuradha M. |
| author_sort | Soudbakhsh, Damoon |
| collection | MIT |
| description | Model predictive control (MPC) has been used in many industrial applications because of its ability to produce optimal performance while accommodating constraints. However, its application on plants with fast time constants is difficult because of its computationally expensive algorithm. In this research, we propose a parallelized MPC that makes use of the structure of the computations and the matrices in the MPC. We show that the computational time of MPC with prediction horizon N can be reduced to O(log(N)) using parallel computing, which is significantly less than that with other available algorithms. |
| first_indexed | 2024-09-23T10:09:52Z |
| format | Article |
| id | mit-1721.1/86999 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:09:52Z |
| publishDate | 2014 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/869992022-09-26T16:07:06Z Parallelized Model Predictive Control Soudbakhsh, Damoon Annaswamy, Anuradha M. Massachusetts Institute of Technology. Department of Mechanical Engineering Annaswamy, Anuradha Soudbakhsh, Damoon Annaswamy, Anuradha M. Model predictive control (MPC) has been used in many industrial applications because of its ability to produce optimal performance while accommodating constraints. However, its application on plants with fast time constants is difficult because of its computationally expensive algorithm. In this research, we propose a parallelized MPC that makes use of the structure of the computations and the matrices in the MPC. We show that the computational time of MPC with prediction horizon N can be reduced to O(log(N)) using parallel computing, which is significantly less than that with other available algorithms. National Science Foundation (U.S.) (Grant ECCS-1135815) 2014-05-15T17:04:18Z 2014-05-15T17:04:18Z 2013-06 Article http://purl.org/eprint/type/ConferencePaper 978-1-4799-0178-4 http://hdl.handle.net/1721.1/86999 Soudbakhsh, Damoon and Anuradha M. Annaswamy. "Parallelized Model Predictive Control." 2013 American Control Conference. IEEE, 2013. https://orcid.org/0000-0002-4354-0459 en_US http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6580083 Proceedings of the 2013 American Control Conference Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers (IEEE) Soudbakhsh |
| spellingShingle | Soudbakhsh, Damoon Annaswamy, Anuradha M. Parallelized Model Predictive Control |
| title | Parallelized Model Predictive Control |
| title_full | Parallelized Model Predictive Control |
| title_fullStr | Parallelized Model Predictive Control |
| title_full_unstemmed | Parallelized Model Predictive Control |
| title_short | Parallelized Model Predictive Control |
| title_sort | parallelized model predictive control |
| url | http://hdl.handle.net/1721.1/86999 https://orcid.org/0000-0002-4354-0459 |
| work_keys_str_mv | AT soudbakhshdamoon parallelizedmodelpredictivecontrol AT annaswamyanuradham parallelizedmodelpredictivecontrol |