Sequential Methods for Probabilistic Design

This chapter studies sequential algorithms for control design of uncertain systems with probabilistic techniques. We introduce a unifying theoretical framework that encompasses most of the sequential algorithms for feasibility that appeared in the literature. In particular, under a convexity assumption in the design parameters, we develop stochastic approximation algorithms that return a so-called reliable design. The notions of probabilistic oracle and update rules are also formally introduced. Gradient algorithms and localization methods are discussed in detail.

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