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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