Robust LPV estimator synthesis using integral quadratic constraints

Raghu Venkataraman; Peter J Seiler Jr

doi:10.1109/ACC.2016.7526079

Robust LPV estimator synthesis using integral quadratic constraints

Raghu Venkataraman, Peter J Seiler Jr

Aerospace Engineering and Mechanics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Scopus citations

Abstract

A method is presented for synthesizing output estimators for a class of continuous time, uncertain, linear parameter-varying (LPV) systems. The uncertain system is described as an interconnection of a nominal LPV system and a block structured perturbation. The nominal LPV system is gridded over the space of parameters, with the state matrices being arbitrary functions of the parameters. The input/output behavior of the perturbation is described by integral quadratic constraints. The main contribution of this paper is the derivation of convex conditions for the synthesis of output estimators for uncertain, grid-based LPV plants. Since LPV systems do not have valid frequency response interpretations, the time domain, dissipation inequality approach is followed. Robust performance is measured using the upper-bound on the worst-case induced-L2 gain of the closed loop. The effectiveness of the proposed method is demonstrated using a numerical example.

Original language	English (US)
Title of host publication	2016 American Control Conference, ACC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4611-4616
Number of pages	6
ISBN (Electronic)	9781467386821
DOIs	https://doi.org/10.1109/ACC.2016.7526079
State	Published - Jul 28 2016
Event	2016 American Control Conference, ACC 2016 - Boston, United States Duration: Jul 6 2016 → Jul 8 2016

Publication series

Name	Proceedings of the American Control Conference
Volume	2016-July
ISSN (Print)	0743-1619

Other

Other	2016 American Control Conference, ACC 2016
Country/Territory	United States
City	Boston
Period	7/6/16 → 7/8/16

Bibliographical note

Funding Information:
The authors would like to thank Marcio Lacerda, Tamas Peni, Harald Pfifer, and Shu Wang for insightful discussions. This work was supported by the National Science Foundation under Grant No. NSF/CNS-1329390 entitled CPS: Breakthrough: Collaborative Research: Managing Uncertainty in the Design of Safety-Critical Aviation Systems

Publisher Copyright:
© 2016 American Automatic Control Council (AACC).

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Robust LPV estimator synthesis using integral quadratic constraints. / Venkataraman, Raghu; Seiler Jr, Peter J.
2016 American Control Conference, ACC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 4611-4616 7526079 (Proceedings of the American Control Conference; Vol. 2016-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Venkataraman, R & Seiler Jr, PJ 2016, Robust LPV estimator synthesis using integral quadratic constraints. in 2016 American Control Conference, ACC 2016., 7526079, Proceedings of the American Control Conference, vol. 2016-July, Institute of Electrical and Electronics Engineers Inc., pp. 4611-4616, 2016 American Control Conference, ACC 2016, Boston, United States, 7/6/16. https://doi.org/10.1109/ACC.2016.7526079

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abstract = "A method is presented for synthesizing output estimators for a class of continuous time, uncertain, linear parameter-varying (LPV) systems. The uncertain system is described as an interconnection of a nominal LPV system and a block structured perturbation. The nominal LPV system is gridded over the space of parameters, with the state matrices being arbitrary functions of the parameters. The input/output behavior of the perturbation is described by integral quadratic constraints. The main contribution of this paper is the derivation of convex conditions for the synthesis of output estimators for uncertain, grid-based LPV plants. Since LPV systems do not have valid frequency response interpretations, the time domain, dissipation inequality approach is followed. Robust performance is measured using the upper-bound on the worst-case induced-L2 gain of the closed loop. The effectiveness of the proposed method is demonstrated using a numerical example.",

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Robust LPV estimator synthesis using integral quadratic constraints

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