ABSTRACT
In this chapter, we study probabilistic and randomized methods for analysis and design of uncertain systems. This area is fairly recent, see [10,11], even though its roots lie in the robustness techniques for handling complex control systems developed in the 1980s. In contrast to these previous deterministic techniques, the main feature of these methods is the use of probabilistic concepts. One of the goals of this methodology is to provide a rapprochement between the classical stochastic and robust paradigms, combining worst-case bounds with probabilistic information, thus potentially reducing the conservatism inherent in the worst-case design. In this way, the control engineer gains additional insight that may help bridging the gap between theory and applications.
