Learning-Based Adaptive Control for 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：

IEEE CONTROL SYSTEMS LETTERS | 2023年 / 7卷

基金：

澳大利亚研究理事会;

关键词：

Stochastic processes; Adaptive control; Upper bound; Linear systems; Closed loop systems; Additives; Stability criteria; stochastic control; constrained control; linear systems; EXTREMUM SEEKING; STABILIZATION;

D O I：

10.1109/LCSYS.2022.3232692

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 propose a certainty-equivalence scheme for adaptive control of scalar linear systems subject to additive, i.i.d. Gaussian disturbances and bounded control input constraints, without requiring prior knowledge of the bounds of the system parameters, nor the control direction. Assuming that the system is at-worst marginally stable, mean square boundedness of the closed-loop system states is proven. Lastly, numerical examples are presented to illustrate our results.

引用

页码：1273 / 1278

页数：6

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