Contraction Analysis of Discrete-Time Stochastic Systems
Yu Kawano, Yohei Hosoe
Abstract
In this paper, we develop a novel contraction framework for stability analysis of discrete-time nonlinear systems with parameters following stochastic processes. For general stochastic processes, we first provide a sufficient condition for uniform incremental exponential stability (UIES) in the first moment with respect to a Riemannian metric. Then, focusing on the Euclidean distance, we present a necessary and sufficient condition for UIES in the second moment. By virtue of studying general stochastic processes, we can readily derive UIES conditions for special classes of processes, e.g., independent and identically distributed (i.i.d.) processes and Markov processes, which is demonstrated as selected applications of our results.