A scenario approach to non-convex control design: Preliminary probabilistic guarantees
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2014
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Conference Paper
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yes
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Abstract
Randomized optimization is a recently established tool for control design with modulated robustness. While for uncertain convex programs there exist randomized approaches with efficient sampling, this is not the case for non-convex problems. Approaches based on statistical learning theory are applicable for a certain class of non-convex problems, but they usually are conservative in terms of performance and are computationally demanding. In this paper, we present a novel scenario approach for a wide class of random non-convex programs. We provide a sample complexity similar to the one for uncertain convex programs, but valid for all feasible solutions inside a set of a-priori chosen complexity. Our scenario approach applies to many non-convex control-design problems, for instance control synthesis based on uncertain bilinear matrix inequalities.
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2014 American Control Conference (ACC)
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3431 - 3436
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IEEE
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2014 American Control Conference (ACC 2014)
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03751 - Lygeros, John / Lygeros, John