This paper provides a new framework for analyzing white noise disturbances in linear systems: rather than the usual stochastic approach, noise signals are described as elements in sets and their effect is analyzed from a worst-case perspective.
The paper studies how these sets must be chosen in order to have adequate properties for system response in the worst-case, statistics consistent with the stochastic point of view, and simple descriptions that allow for tractable worst-case analysis. The methodology is demonstrated by considering its implications in two problems: rejection of white noise signals in the presence of system uncertainty, and worst-case system identification
