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• W2783274227 abstract "We study the problem of designing optimal structured feedback gains for large-scale systems in a distributed manner. We quantify performance using the H 2 norm and introduce regularization functions to promote desired structural properties in the controller. The key challenge is to evaluate the objective function and its gradient without solving the large-scale Lyapunov equations. We exploit the square-additive property of the H 2 norm and provide an equivalent consensus-based characterization that is convenient for distributed implementation. The resulting constrained optimal control problem is in general non-convex and we solve it using an algorithm based on the alternating direction method of multipliers. This algorithm exploits separability of the objective function and is guaranteed to converge to a local minimum. For the optimal control of symmetric systems and undirected consensus networks we establish convexity and demonstrate how the underlying structure can be exploited to further simplify computations." @default.
• W2783274227 created "2018-01-26" @default.
• W2783274227 creator A5009561411 @default.
• W2783274227 creator A5087790067 @default.
• W2783274227 date "2017-12-01" @default.
• W2783274227 modified "2023-09-25" @default.
• W2783274227 title "Distributed design of optimal structured feedback gains" @default.
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• W2783274227 doi "https://doi.org/10.1109/cdc.2017.8264652" @default.
• W2783274227 hasPublicationYear "2017" @default.
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