Robust control design for linear systems via multiplicative noise

Benjamin J. Gravell; Peyman Mohajerin Esfahani; Tyler H. Summers

doi:10.1016/j.ifacol.2020.12.1268

Robust control design for linear systems via multiplicative noise

Benjamin J. Gravell^*, Peyman Mohajerin Esfahani, Tyler H. Summers

^*Corresponding author for this work

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

4 Citations (Scopus)

26 Downloads (Pure)

Abstract

Robust stability and stochastic stability have separately seen intense study in control theory for many decades. In this work we establish relations between these properties for discrete-time systems and employ them for robust control design. Specifically, we examine a multiplicative noise framework which models the inherent uncertainty and variation in the system dynamics which arise in model-based learning control methods such as adaptive control and reinforcement learning. We provide results which guarantee robustness margins in terms of perturbations on the nominal dynamics as well as algorithms which generate maximally robust controllers.

Original language	English
Pages (from-to)	7392-7399
Journal	IFAC-PapersOnline
Volume	53 (2020)
Issue number	2
DOIs	https://doi.org/10.1016/j.ifacol.2020.12.1268
Publication status	Published - 2021
Event	21st IFAC World Congress 2020 - Berlin, Germany Duration: 12 Jul 2020 → 17 Jul 2020

Keywords

Robust control (linear case)
Robust controller synthesis
Stochastic systems
Uncertainty descriptions

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10.1016/j.ifacol.2020.12.1268

1-s2.0-S2405896320316700-mainFinal published version, 481 KBLicence: CC BY-NC-ND

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title = "Robust control design for linear systems via multiplicative noise",

abstract = "Robust stability and stochastic stability have separately seen intense study in control theory for many decades. In this work we establish relations between these properties for discrete-time systems and employ them for robust control design. Specifically, we examine a multiplicative noise framework which models the inherent uncertainty and variation in the system dynamics which arise in model-based learning control methods such as adaptive control and reinforcement learning. We provide results which guarantee robustness margins in terms of perturbations on the nominal dynamics as well as algorithms which generate maximally robust controllers.",

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AU - Gravell, Benjamin J.

AU - Esfahani, Peyman Mohajerin

AU - Summers, Tyler H.

PY - 2021

Y1 - 2021

N2 - Robust stability and stochastic stability have separately seen intense study in control theory for many decades. In this work we establish relations between these properties for discrete-time systems and employ them for robust control design. Specifically, we examine a multiplicative noise framework which models the inherent uncertainty and variation in the system dynamics which arise in model-based learning control methods such as adaptive control and reinforcement learning. We provide results which guarantee robustness margins in terms of perturbations on the nominal dynamics as well as algorithms which generate maximally robust controllers.

AB - Robust stability and stochastic stability have separately seen intense study in control theory for many decades. In this work we establish relations between these properties for discrete-time systems and employ them for robust control design. Specifically, we examine a multiplicative noise framework which models the inherent uncertainty and variation in the system dynamics which arise in model-based learning control methods such as adaptive control and reinforcement learning. We provide results which guarantee robustness margins in terms of perturbations on the nominal dynamics as well as algorithms which generate maximally robust controllers.

KW - Robust control (linear case)

KW - Robust controller synthesis

KW - Stochastic systems

KW - Uncertainty descriptions

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DO - 10.1016/j.ifacol.2020.12.1268

M3 - Conference article

AN - SCOPUS:85102803556

VL - 53 (2020)

SP - 7392

EP - 7399

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 1474-6670

IS - 2

T2 - 21st IFAC World Congress 2020

Y2 - 12 July 2020 through 17 July 2020

ER -

Robust control design for linear systems via multiplicative noise

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Keywords

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