Details
| Original language | English |
|---|---|
| Article number | 100992 |
| Journal | Annual reviews in control |
| Volume | 60 |
| Publication status | Published - 23 May 2025 |
Abstract
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 in the design of 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.
Keywords
- Convex optimization, Linear systems, Model predictive control, Optimal control, Polyhedral computing, Robust control
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Engineering(all)
- Control and Systems Engineering
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In: Annual reviews in control, Vol. 60, 100992, 23.05.2025.
Research output: Contribution to journal › Review article › Research › peer review
}
TY - JOUR
T1 - Polyhedral control design
T2 - Theory and methods
AU - Houska, Boris
AU - Müller, Matthias A.
AU - Villanueva, Mario Eduardo
N1 - Publisher Copyright: © 2025 The Authors
PY - 2025/5/23
Y1 - 2025/5/23
N2 - 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 in the design of 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.
AB - 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 in the design of 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.
KW - Convex optimization
KW - Linear systems
KW - Model predictive control
KW - Optimal control
KW - Polyhedral computing
KW - Robust control
UR - http://www.scopus.com/inward/record.url?scp=105005755885&partnerID=8YFLogxK
U2 - 10.1016/j.arcontrol.2025.100992
DO - 10.1016/j.arcontrol.2025.100992
M3 - Review article
AN - SCOPUS:105005755885
VL - 60
JO - Annual reviews in control
JF - Annual reviews in control
SN - 1367-5788
M1 - 100992
ER -