Convex Optimization Approaches to Information Structured Decentralized Control
| Autor: | Mario Sznaier, José Andrés González López, Yin Wang |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Lyapunov function
0209 industrial biotechnology Mathematical optimization Computational complexity theory Computer science 010103 numerical & computational mathematics 02 engineering and technology Invariant (physics) 01 natural sciences Decentralised system Computer Science Applications symbols.namesake 020901 industrial engineering & automation Quadratic equation Control and Systems Engineering Convex optimization symbols Relaxation (approximation) 0101 mathematics Electrical and Electronic Engineering Convex function Sparse matrix |
| Zdroj: | IEEE Transactions on Automatic Control. 63:3393-3403 |
| ISSN: | 2334-3303 0018-9286 |
| DOI: | 10.1109/tac.2018.2830112 |
| Popis: | This paper considers the problem of synthesizing output feedback controllers subject to sparsity constraints. This problem is known to be generically NP-hard, unless the plant satisfies the quadratic invariance property. Our main results show that, even if this property does not hold, tractable convex relaxations with optimality certificates can be obtained by recasting the problem into a polynomial optimization through the use of polyhedral Lyapunov functions. Combining these ideas with rank minimization tools leads to a computationally attractive algorithm. As an alternative, we present a second relaxation, with lower computational complexity, based on finding the best sparse estimate of a desired control action. These results are illustrated with several examples. |
| Databáze: | OpenAIRE |
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