Randomized Methods for Control of Uncertain Systems

In this article, we study the tools and methodologies for the analysis and design of control systems in the presence of random uncertainty. For analysis, the methods are largely based on the Monte Carlo simulation approach, while for design new randomized algorithms have been developed. These methods have been successfully employed in various application areas, which include systems biology; aerospace control; control of hard disk drives; high-speed networks; quantized, embedded, and electric circuits; structural design; and automotive and driver assistance.