Distribution Modeling and Stabilization Control for Discrete-Time Linear Random Dynamical Systems Using Ensemble Kalman Filter
For researchers in stochastic control, this work provides a first step toward learning-type control using ensemble Kalman filters, but the results are preliminary and incremental.
This paper develops an output feedback stabilization control framework for discrete-time linear systems with i.i.d. stochastic dynamics, using an ensemble Kalman filter for both distribution modeling and state estimation. Numerical experiments demonstrate the effectiveness of the approach.
This paper studies an output feedback stabilization control framework for discrete-time linear systems with stochastic dynamics determined by an independent and identically distributed (i.i.d.) process. The controller is constructed with an ensemble Kalman filter (EnKF) and a feedback gain designed with our earlier result about state feedback control. The EnKF is also used for modeling the distribution behind the system, which is required in the feedback gain synthesis. The effectiveness of our control framework is demonstrated with numerical experiments. This study will become the first step toward the realization of learning type control using our stochastic systems control theory.