Regret Analysis of Online LQR Control via Trajectory Prediction and Tracking

被引：0
|
作者：
Chen, Yitian [1 ]
Molloy, Timothy L. [1 ]
Summers, Tyler [2 ]
Shames, Iman [1 ]
机构：
[1] Australian Natl Univ, CIICADA Lab, Canberra, ACT, Australia
[2] Univ Texas Dallas, Richardson, TX 75083 USA
来源：
LEARNING FOR DYNAMICS AND CONTROL CONFERENCE, VOL 211 | 2023年 / 211卷
基金：
澳大利亚研究理事会;
关键词：
Online LQR; Dynamic Regret; Trajectory tracking; MPC;
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：
In this paper, we propose and analyse a new method for online linear quadratic regulator (LQR) control with a priori unknown time-varying cost matrices. The cost matrices are revealed sequentially with the potential for future values to be previewed over a short window. Our novel method involves using the available cost matrices to predict the optimal trajectory, and a tracking controller to drive the system towards it. We adopted the notion of dynamic regret to measure the performance of this proposed online LQR control method, with our main result being that the (dynamic) regret of our method is upper bounded by a constant. Moreover, the regret upper bound decays exponentially with the preview window length, and is extendable to systems with disturbances. We show in simulations that our proposed method offers improved performance compared to other previously proposed online LQR methods.
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页数：11
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