A Characterization of Convex Problems in Decentralized Control ^ast
We consider the problem of constructing optimal decentralized controllers. We formulate this problem as one of minimizing the closed-loop norm of a feedback system subject to constraints on the controller structure. We define the notion of quadratic invariance of a constraint set with respect to a s...
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| Published in | IEEE transactions on automatic control Vol. 51; no. 2; pp. 274 - 286 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | English Japanese |
| Published |
IEEE
01.02.2006
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 0018-9286 1558-2523 |
| DOI | 10.1109/TAC.2005.860365 |
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| Abstract | We consider the problem of constructing optimal decentralized controllers. We formulate this problem as one of minimizing the closed-loop norm of a feedback system subject to constraints on the controller structure. We define the notion of quadratic invariance of a constraint set with respect to a system, and show that if the constraint set has this property, then the constrained minimum-norm problem may be solved via convex programming. We also show that quadratic invariance is necessary and sufficient for the constraint set to be preserved under feedback. These results are developed in a very general framework, and are shown to hold in both continuous and discrete time, for both stable and unstable systems, and for any norm. This notion unifies many previous results identifying specific tractable decentralized control problems, and delineates the largest known class of convex problems in decentralized control. As an example, we show that optimal stabilizing controllers may be efficiently computed in the case where distributed controllers can communicate faster than their dynamics propagate. We also show that symmetric synthesis is included in this classification, and provide a test for sparsity constraints to be quadratically invariant, and thus amenable to convex synthesis. |
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| AbstractList | We consider the problem of constructing optimal decentralized controllers. We formulate this problem as one of minimizing the closed-loop norm of a feedback system subject to constraints on the controller structure. We define the notion of quadratic invariance of a constraint set with respect to a system, and show that if the constraint set has this property, then the constrained minimum-norm problem may be solved via convex programming. We also show that quadratic invariance is necessary and sufficient for the constraint set to be preserved under feedback. These results are developed in a very general framework, and are shown to hold in both continuous and discrete time, for both stable and unstable systems, and for any norm. This notion unifies many previous results identifying specific tractable decentralized control problems, and delineates the largest known class of convex problems in decentralized control. As an example, we show that optimal stabilizing controllers may be efficiently computed in the case where distributed controllers can communicate faster than their dynamics propagate. We also show that symmetric synthesis is included in this classification, and provide a test for sparsity constraints to be quadratically invariant, and thus amenable to convex synthesis. |
| Author | Rotkowitz, M. Lall, S. |
| Author_xml | – sequence: 1 givenname: M. surname: Rotkowitz fullname: Rotkowitz, M. – sequence: 2 givenname: S. surname: Lall fullname: Lall, S. |
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| SubjectTerms | Automatic control Control systems Convex optimization decentralized control delayed control Distributed computing Distributed control Error correction extended linear spaces Feedback Network synthesis networked control Optimal control Quadratic programming Space vehicles |
| Title | A Characterization of Convex Problems in Decentralized Control ^ast |
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