Distribution Steering for Discrete-Time Uncertain Ensemble Systems
Guangyu Wu, Panagiotis Tsiotras, Anders Lindquist

TL;DR
This paper develops a method to steer the probability distribution of discrete-time uncertain ensemble systems, using moments to handle non-Gaussian distributions and providing a control law for distribution shaping.
Contribution
It introduces a novel distribution steering approach for discrete-time ensemble systems with arbitrary distributions, extending beyond Gaussian assumptions.
Findings
Method effectively handles non-Gaussian distributions.
Finite-dimensional control law derived for moment system.
Numerical example validates theoretical approach.
Abstract
Ensemble systems appear frequently in many engineering applications and, as a result, they have become an important research topic in control theory. These systems are best characterized by the evolution of their underlying state distribution. Despite the work to date, few results exist dealing with the problem of directly modifying (i.e., ``steering'') the distribution of an ensemble system. In addition, in most existing results, the distribution of the states of an ensemble of discrete-time systems is assumed to be Gaussian. However, in case the system parameters are uncertain, it is not always realistic to assume that the distribution of the system follows a Gaussian distribution, thus complicating the solution of the overall problem. In this paper, we address the general distribution steering problem for first-order discrete-time ensemble systems, where the distributions of the…
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Taxonomy
TopicsSimulation Techniques and Applications · Smart Grid Security and Resilience · Stability and Control of Uncertain Systems
