Downloads: 163
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
LCSYS.2018.2833621.pdf | 196.5 kB | Adobe PDF | View/Open |
Title: | Sparsity-Constrained Controllability Maximization With Application to Time-Varying Control Node Selection |
Authors: | Ikeda, Takuya Kashima, Kenji ![]() ![]() ![]() |
Author's alias: | 池田, 卓矢 加嶋, 健司 |
Keywords: | Optimal control linear systems time-varying systems sparse optimal control networked control systems |
Issue Date: | Jul-2018 |
Publisher: | Institute of Electrical and Electronics Engineers(IEEE) |
Journal title: | IEEE Control Systems Letters |
Volume: | 2 |
Issue: | 3 |
Start page: | 321 |
End page: | 326 |
Abstract: | In this letter, we consider the maximization of a quantitative metric of controllability with a constraint of L 0 norm of the control input. Since the optimization problem contains a combinatorial structure, we introduce a convex relaxation problem for the sake of reducing computation burden. We prove the existence of solutions to the main problem and also give a simple condition under which the relaxed problem gives a solution to the main problem. It should be emphasized that the main problem can formulate time-varying control node selection, which attempts to extract when and where exogenous inputs should be provided in order to achieve high controllability of multi-agent systems. |
Rights: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is not the published version. Please cite only the published version. この論文は出版社版でありません。引用の際には出版社版をご確認ご利用ください。 |
URI: | http://hdl.handle.net/2433/245128 |
DOI(Published Version): | 10.1109/LCSYS.2018.2833621 |
Appears in Collections: | Journal Articles |

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.