Polyhedral Control Design: Theory and Methods
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917871201812480 |
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| author | Houska, Boris Müller, Matthias A. Villanueva, Mario E. |
| author_facet | Houska, Boris Müller, Matthias A. Villanueva, Mario E. |
| contents | In this article, we survey the primary research on polyhedral computing methods for constrained linear control systems. Our focus is on the modeling power of convex optimization, featured to design set-based robust and optimal controllers. In detail, we review the state-of-the-art techniques for computing geometric structures such as robust control invariant polytopes. Moreover, we survey recent methods for constructing control Lyapunov functions with polyhedral epigraphs as well as the extensive literature on robust model predictive control. The article concludes with a discussion of both the complexity and potential of polyhedral computing methods that rely on large-scale convex optimization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_13082 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Polyhedral Control Design: Theory and Methods Houska, Boris Müller, Matthias A. Villanueva, Mario E. Optimization and Control Systems and Control In this article, we survey the primary research on polyhedral computing methods for constrained linear control systems. Our focus is on the modeling power of convex optimization, featured to design set-based robust and optimal controllers. In detail, we review the state-of-the-art techniques for computing geometric structures such as robust control invariant polytopes. Moreover, we survey recent methods for constructing control Lyapunov functions with polyhedral epigraphs as well as the extensive literature on robust model predictive control. The article concludes with a discussion of both the complexity and potential of polyhedral computing methods that rely on large-scale convex optimization. |
| title | Polyhedral Control Design: Theory and Methods |
| topic | Optimization and Control Systems and Control |
| url | https://arxiv.org/abs/2412.13082 |