Tuning LQR Controllers: A Sensitivity-Based Approach

被引：5

作者：

Masti, Daniele ^{[1
]}

Zanon, Mario ^{[1
]}

Bemporad, Alberto ^{[1
,2
]}

机构：

[1] IMT Sch Adv Studies Lucca, Dynam Syst Control & Optimizat Res Unit, I-55100 Lucca, Italy

[2] IMT Sch Adv Studies Lucca, Dept Comp Sci & Engn, I-55100 Lucca, Italy

来源：

IEEE CONTROL SYSTEMS LETTERS | 2022年 / 6卷

关键词：

Tuning; Optimization; Trajectory; Time-domain analysis; Stability criteria; Regulators; Performance analysis; Identification for control; machine learning; MODEL-PREDICTIVE CONTROL; ALGORITHM;

D O I：

10.1109/LCSYS.2021.3087556

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 introduce an approach to efficiently tune LQR controllers for linear time-invariant systems to match a prescribed closed-loop behavior, such as the one given by a reference model. The proposed approach is able to efficiently tune the LQR controller, even for high dimensional systems and is superior in terms of achieved tracking performance and other criteria with respect to global optimization methods commonly used for black-box, simulation-based, automated tuning.

引用

页码：932 / 937

页数：6

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