Stability Bounds for Learning-Based Adaptive Control of Discrete-Time Multi-Dimensional Stochastic Linear Systems with Input Constraints
Systems and Control
2023-04-04 v1 Machine Learning
Systems and Control
Optimization and Control
Abstract
We consider the problem of adaptive stabilization for discrete-time, multi-dimensional linear systems with bounded control input constraints and unbounded stochastic disturbances, where the parameters of the true system are unknown. To address this challenge, we propose a certainty-equivalent control scheme which combines online parameter estimation with saturated linear control. We establish the existence of a high probability stability bound on the closed-loop system, under additional assumptions on the system and noise processes. Finally, numerical examples are presented to illustrate our results.
Cite
@article{arxiv.2304.00569,
title = {Stability Bounds for Learning-Based Adaptive Control of Discrete-Time Multi-Dimensional Stochastic Linear Systems with Input Constraints},
author = {Seth Siriya and Jingge Zhu and Dragan Nešić and Ye Pu},
journal= {arXiv preprint arXiv:2304.00569},
year = {2023}
}
Comments
21 pages, 1 figure, submitted to 62nd IEEE Conference on Decision and Control