GP-based MPC with updating tube for safety control of unknown system
The Gaussian process model inferred from the Bayesian framework is a powerful data modeling method. It provides not only the predictive value but also the uncertainty measure for the predictive result. In this paper, we combine the online-updating GP models with Tube MPC to achieve safety control fo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-09-01
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| Series: | Digital Chemical Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772508122000321 |
| _version_ | 1828365603624189952 |
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| author | Yi Zheng Tongqiang Zhang Shaoyuan Li Guanlin Zhang Yanye Wang |
| author_facet | Yi Zheng Tongqiang Zhang Shaoyuan Li Guanlin Zhang Yanye Wang |
| author_sort | Yi Zheng |
| collection | DOAJ |
| description | The Gaussian process model inferred from the Bayesian framework is a powerful data modeling method. It provides not only the predictive value but also the uncertainty measure for the predictive result. In this paper, we combine the online-updating GP models with Tube MPC to achieve safety control for the system of unknown dynamics, and especially use its capability of uncertainty quantification to assist in the safety guarantee under certain probability. Tightened constraints for safety used to determine the center of the Tube are computed according to the designed method based on the mean and variance predictive function of GP models. The constraints are updated at every control period based on the updated GP models. Meanwhile, a specific updating mechanism of the data set is adopted to accomplish effective updating. Finally, an example of a vehicle system model is used to verify the proposed method. |
| first_indexed | 2024-04-14T05:33:23Z |
| format | Article |
| id | doaj.art-f1c752c107e94a02ae3bd7815a8a97f5 |
| institution | Directory Open Access Journal |
| issn | 2772-5081 |
| language | English |
| last_indexed | 2024-04-14T05:33:23Z |
| publishDate | 2022-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Digital Chemical Engineering |
| spelling | doaj.art-f1c752c107e94a02ae3bd7815a8a97f52022-12-22T02:09:44ZengElsevierDigital Chemical Engineering2772-50812022-09-014100041GP-based MPC with updating tube for safety control of unknown systemYi Zheng0Tongqiang Zhang1Shaoyuan Li2Guanlin Zhang3Yanye Wang4Corresponding author.; Department of Automation, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, ChinaDepartment of Automation, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, ChinaDepartment of Automation, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, ChinaDepartment of Automation, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, ChinaDepartment of Automation, Shanghai Jiao Tong University, Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, ChinaThe Gaussian process model inferred from the Bayesian framework is a powerful data modeling method. It provides not only the predictive value but also the uncertainty measure for the predictive result. In this paper, we combine the online-updating GP models with Tube MPC to achieve safety control for the system of unknown dynamics, and especially use its capability of uncertainty quantification to assist in the safety guarantee under certain probability. Tightened constraints for safety used to determine the center of the Tube are computed according to the designed method based on the mean and variance predictive function of GP models. The constraints are updated at every control period based on the updated GP models. Meanwhile, a specific updating mechanism of the data set is adopted to accomplish effective updating. Finally, an example of a vehicle system model is used to verify the proposed method.http://www.sciencedirect.com/science/article/pii/S2772508122000321GP modelsTube MPCOnline updatingSafety control |
| spellingShingle | Yi Zheng Tongqiang Zhang Shaoyuan Li Guanlin Zhang Yanye Wang GP-based MPC with updating tube for safety control of unknown system Digital Chemical Engineering GP models Tube MPC Online updating Safety control |
| title | GP-based MPC with updating tube for safety control of unknown system |
| title_full | GP-based MPC with updating tube for safety control of unknown system |
| title_fullStr | GP-based MPC with updating tube for safety control of unknown system |
| title_full_unstemmed | GP-based MPC with updating tube for safety control of unknown system |
| title_short | GP-based MPC with updating tube for safety control of unknown system |
| title_sort | gp based mpc with updating tube for safety control of unknown system |
| topic | GP models Tube MPC Online updating Safety control |
| url | http://www.sciencedirect.com/science/article/pii/S2772508122000321 |
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