Applications of System Identification with Sparse Bayesian Regression Discovery of Unmodeled Dynamics of an Airship

Steven Messinger; Mathew Fehl; Simon W. Miller; Michael Zugger; Michael Yukish

doi:10.23919/ACC53348.2022.9867598

Applications of System Identification with Sparse Bayesian Regression Discovery of Unmodeled Dynamics of an Airship

Steven Messinger, Mathew Fehl, Simon W. Miller, Michael Zugger, Michael Yukish

Applied Research Laboratory (ARL)

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

Abstract

In this paper, we consider a combination of traditional and modern methods to perform data-driven system identification (SysID) of a prototype lighter-than-air vehicle. We explore the methods of linear least squares (LS), subsampling based threshold sparse Bayesian regression (SubTSBR), and a novel implementation using both methods to form a constrained optimization problem. Notably, linear LS system identification is used to solve for parameters that are defined by a proposed dynamic model and SubTSBR is used to discover remaining unmodeled dynamics given the error in the LS model. This allows for a high fidelity model of the prototype LTAV from flight test data that outperforms the LS method and reduces negative effects of sparse SysID.

Original language	English (US)
Title of host publication	2022 American Control Conference, ACC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1414-1419
Number of pages	6
ISBN (Electronic)	9781665451963
DOIs	https://doi.org/10.23919/ACC53348.2022.9867598
State	Published - 2022
Event	2022 American Control Conference, ACC 2022 - Atlanta, United States Duration: Jun 8 2022 → Jun 10 2022

Publication series

Name	Proceedings of the American Control Conference
Volume	2022-June
ISSN (Print)	0743-1619

Conference

Conference	2022 American Control Conference, ACC 2022
Country/Territory	United States
City	Atlanta
Period	6/8/22 → 6/10/22

All Science Journal Classification (ASJC) codes

Electrical and Electronic Engineering

Access to Document

10.23919/ACC53348.2022.9867598

Cite this

Messinger, S., Fehl, M., Miller, S. W., Zugger, M., & Yukish, M. (2022). Applications of System Identification with Sparse Bayesian Regression Discovery of Unmodeled Dynamics of an Airship. In 2022 American Control Conference, ACC 2022 (pp. 1414-1419). (Proceedings of the American Control Conference; Vol. 2022-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/ACC53348.2022.9867598

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title = "Applications of System Identification with Sparse Bayesian Regression Discovery of Unmodeled Dynamics of an Airship",

abstract = "In this paper, we consider a combination of traditional and modern methods to perform data-driven system identification (SysID) of a prototype lighter-than-air vehicle. We explore the methods of linear least squares (LS), subsampling based threshold sparse Bayesian regression (SubTSBR), and a novel implementation using both methods to form a constrained optimization problem. Notably, linear LS system identification is used to solve for parameters that are defined by a proposed dynamic model and SubTSBR is used to discover remaining unmodeled dynamics given the error in the LS model. This allows for a high fidelity model of the prototype LTAV from flight test data that outperforms the LS method and reduces negative effects of sparse SysID.",

author = "Steven Messinger and Mathew Fehl and Miller, {Simon W.} and Michael Zugger and Michael Yukish",

note = "Publisher Copyright: {\textcopyright} 2022 American Automatic Control Council.; 2022 American Control Conference, ACC 2022 ; Conference date: 08-06-2022 Through 10-06-2022",

year = "2022",

doi = "10.23919/ACC53348.2022.9867598",

language = "English (US)",

series = "Proceedings of the American Control Conference",

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Messinger, S, Fehl, M, Miller, SW, Zugger, M & Yukish, M 2022, Applications of System Identification with Sparse Bayesian Regression Discovery of Unmodeled Dynamics of an Airship. in 2022 American Control Conference, ACC 2022. Proceedings of the American Control Conference, vol. 2022-June, Institute of Electrical and Electronics Engineers Inc., pp. 1414-1419, 2022 American Control Conference, ACC 2022, Atlanta, United States, 6/8/22. https://doi.org/10.23919/ACC53348.2022.9867598

Applications of System Identification with Sparse Bayesian Regression Discovery of Unmodeled Dynamics of an Airship. / Messinger, Steven; Fehl, Mathew; Miller, Simon W. et al.
2022 American Control Conference, ACC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 1414-1419 (Proceedings of the American Control Conference; Vol. 2022-June).

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

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AB - In this paper, we consider a combination of traditional and modern methods to perform data-driven system identification (SysID) of a prototype lighter-than-air vehicle. We explore the methods of linear least squares (LS), subsampling based threshold sparse Bayesian regression (SubTSBR), and a novel implementation using both methods to form a constrained optimization problem. Notably, linear LS system identification is used to solve for parameters that are defined by a proposed dynamic model and SubTSBR is used to discover remaining unmodeled dynamics given the error in the LS model. This allows for a high fidelity model of the prototype LTAV from flight test data that outperforms the LS method and reduces negative effects of sparse SysID.

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Messinger S, Fehl M, Miller SW, Zugger M, Yukish M. Applications of System Identification with Sparse Bayesian Regression Discovery of Unmodeled Dynamics of an Airship. In 2022 American Control Conference, ACC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 1414-1419. (Proceedings of the American Control Conference). doi: 10.23919/ACC53348.2022.9867598

Applications of System Identification with Sparse Bayesian Regression Discovery of Unmodeled Dynamics of an Airship

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