Stability Bounds for Learning-Based Adaptive Control of Discrete-Time Multi-Dimensional Stochastic Linear Systems with Input Constraints

被引：0
作者：
Siriya, Seth [1 ]
Zhu, Jingge [1 ]
Nesic, Dragan [1 ]
Pu, Ye [1 ]
机构：
[1] Univ Melbourne, Dept Elect & Elect Engn, Parkville, Vic 3010, Australia
来源：
2023 62ND IEEE CONFERENCE ON DECISION AND CONTROL, CDC | 2023年
基金：
澳大利亚研究理事会;
关键词：
MEAN-SQUARE BOUNDEDNESS; EXTREMUM SEEKING; STABILIZATION;
D O I：
10.1109/CDC49753.2023.10383870
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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 system are unknown. To address this challenge, we propose a certainty-equivalent control scheme combining 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. Numerical examples are presented to illustrate our results.
引用
收藏
页码：3802 / 3807
页数：6
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