Journal article
Robust MPC with recursive model update
- Abstract:
- Robust constrained control of linear systems with parametric uncertainty and additive disturbance is addressed. The main contribution is the introduction of a mathematically rigorous and computationally tractable framework for stabilizing model predictive control with online parameter estimation to improve performance and reduce conservatism. Requirements for closed-loop stability and provable constraint satisfaction are considered separately, resulting in the use of online set-membership system identification combined with homothetic prediction tubes for robust constraint satisfaction, and an $\mathcal H_\infty$ optimal point estimate of the unknown parameters to achieve a finite closed-loop gain from the disturbance to the state. Extensions to time-varying parameters and persistently exciting inputs to guarantee parameter convergence are presented. A numerical example illustrates the proven properties and efficacy of the approach.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Accepted manuscript, pdf, 494.7KB, Terms of use)
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- Publisher copy:
- 10.1016/j.automatica.2019.02.023
Authors
- Publisher:
- Elsevier
- Journal:
- Automatica More from this journal
- Volume:
- 103
- Pages:
- 461-471
- Publication date:
- 2019-03-05
- Acceptance date:
- 2019-01-24
- DOI:
- ISSN:
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0005-1098
- Keywords:
- Pubs id:
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pubs:965898
- UUID:
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uuid:78236757-fb85-4510-8e17-d75177b667d8
- Local pid:
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pubs:965898
- Source identifiers:
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965898
- Deposit date:
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2019-01-25
Terms of use
- Copyright holder:
- Elsevier Ltd
- Copyright date:
- 2019
- Rights statement:
- © 2019 Elsevier Ltd. All rights reserved.
- Notes:
- This is the accepted manuscript version of the article. The final version is available online from Elsevier at: https://doi.org/10.1016/j.automatica.2019.02.023
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