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.

Polyhedral control design: theory and methods / Houska, Boris; Müller Matthias, A.; Villanueva, Mario Eduardo. - In: ANNUAL REVIEWS IN CONTROL. - ISSN 1367-5788. - 60:(2025). [10.1016/j.arcontrol.2025.100992]

Polyhedral control design: theory and methods

Houska Boris;Villanueva Mario Eduardo
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.
2025
Convex optimization
Linear systems
Model predictive control
Optimal control
Polyhedral computing
Robust control
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11771/39858
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