Demand for Randomized Algorithms for Analysis and Control of Uncertain Systems comes from control engineers who wish to apply more workable methods to a variety of uncertainties and from engineers who need a path between the demands of robust control and the unnecessary complications of optimal control. This book talks about these systems.

The presence of uncertainty in a system description has always been a critical issue in control. Moving on from earlier stochastic and robust control paradigms, the main objective of this book is to introduce the reader to the fundamentals of probabilistic methods in the analysis and design of uncertain systems. Using so-called randomized algorithms, this emerging area of research guarantees a reduction in the computational complexity of classical robust control algorithms and in the conservativeness of methods like H�� control. Features: self-contained treatment explaining the genesis of randomized algorithms in the principles of probability theory to their use for robust analysis and controller synthesis; comprehensive treatment of sample generation, including consideration of the difficulties involved in obtaining identically and independently distributed samples; applications of randomized algorithms in congestion control of high-speed communications networks and the stability of quantized sampled-data systems. Randomized Algorithms for Analysis and Control of Uncertain Systems will be of certain interest to control theorists concerned with robust and optimal control techniques and to all control engineers dealing with system uncertainties.