Learning-Enabled Robust Control with Noisy Measurements

被引：0

作者：

Kjellqvist, Olle ^{[1
]}

Rantzer, Anders ^{[1
]}

机构：

[1] Lund Univ, Automat Control LTH, Box 118, SE-22100 Lund, Sweden

来源：

LEARNING FOR DYNAMICS AND CONTROL CONFERENCE, VOL 168 | 2022年 / 168卷

基金：

欧洲研究理事会;

关键词：

adaptive control; real-time learning;

D O I：

暂无

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 present a constructive approach to bounded l(2)-gain adaptive control with noisy measurements for linear time-invariant scalar systems with uncertain parameters belonging to a finite set. The gain bound refers to the closed-loop system, including the learning procedure. The approach is based on forward dynamic programming to construct a finite-dimensional information state consisting of H-infinity-observers paired with a recursively computed performance metric. We do not assume prior knowledge of a stabilizing controller.

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页数：11

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