Randomized Algorithms for Optimal Robust Performance Controller Design

摘要

In this paper efficient randomized algorithms are constructed to solve optimal robust performance controller design problems. The randomized algorithms here are based on a property from statistical learning theory known as uniform convergence of empirical means (UCEM). It is argued that in order to assess the performance of a controller as the plant varies over a prespecified family, it is better to use the average performance of the controller as the objective function to be minimized, rather than its worst-case performance. The approach is illustrated to be efficient through an example.