Joint identification and control in hybrid linear systems

Christoforos Somarakis; Ion Matei; Maksym Zhenirovskyy; Johan de Kleer; Souma Chowdhury; Rahul Rai

doi:10.1016/j.ifacol.2020.12.1303

Joint identification and control in hybrid linear systems

Christoforos Somarakis
, Ion Matei
, Maksym Zhenirovskyy
, Johan de Kleer
, Souma Chowdhury
, Rahul Rai

Department of Mechanical and Aerospace Engineering

University at Buffalo

Research output: Contribution to journal › Conference article › peer-review

Abstract

We propose a theoretical framework for joint system identification and control on a class of stochastic linear systems. We investigate optimization algorithms for inferring endogenous and environmental parameters from data, part of which are used for control purposes. A number of non-trivial interplays among stability and performance, as well as computational challenges and fundamental limits in identification rate emerge. Our results are validated via simulation example on a quadcopter control problem.

Original language	English
Pages (from-to)	1084-1089
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	53
Issue number	2
DOIs	https://doi.org/10.1016/j.ifacol.2020.12.1303
State	Published - 2020
Event	21st IFAC World Congress 2020 - Berlin, Germany Duration: Jul 12 2020 → Jul 17 2020

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10.1016/j.ifacol.2020.12.1303

Cite this

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abstract = "We propose a theoretical framework for joint system identification and control on a class of stochastic linear systems. We investigate optimization algorithms for inferring endogenous and environmental parameters from data, part of which are used for control purposes. A number of non-trivial interplays among stability and performance, as well as computational challenges and fundamental limits in identification rate emerge. Our results are validated via simulation example on a quadcopter control problem.",

author = "Christoforos Somarakis and Ion Matei and Maksym Zhenirovskyy and \{de Kleer\}, Johan and Souma Chowdhury and Rahul Rai",

note = "Publisher Copyright: Copyright {\textcopyright} 2020 The Authors. This is an open access article under the CC BY-NC-ND license; 21st IFAC World Congress 2020 ; Conference date: 12-07-2020 Through 17-07-2020",

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AU - Somarakis, Christoforos

AU - Matei, Ion

AU - Zhenirovskyy, Maksym

AU - de Kleer, Johan

AU - Chowdhury, Souma

AU - Rai, Rahul

PY - 2020

Y1 - 2020

N2 - We propose a theoretical framework for joint system identification and control on a class of stochastic linear systems. We investigate optimization algorithms for inferring endogenous and environmental parameters from data, part of which are used for control purposes. A number of non-trivial interplays among stability and performance, as well as computational challenges and fundamental limits in identification rate emerge. Our results are validated via simulation example on a quadcopter control problem.

AB - We propose a theoretical framework for joint system identification and control on a class of stochastic linear systems. We investigate optimization algorithms for inferring endogenous and environmental parameters from data, part of which are used for control purposes. A number of non-trivial interplays among stability and performance, as well as computational challenges and fundamental limits in identification rate emerge. Our results are validated via simulation example on a quadcopter control problem.

UR - https://www.scopus.com/pages/publications/85105044209

U2 - 10.1016/j.ifacol.2020.12.1303

DO - 10.1016/j.ifacol.2020.12.1303

M3 - Conference article

SN - 2405-8971

VL - 53

SP - 1084

EP - 1089

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 2

T2 - 21st IFAC World Congress 2020

Y2 - 12 July 2020 through 17 July 2020

ER -

Joint identification and control in hybrid linear systems

Abstract

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