Local E-optimality conditions for trajectory design to estimate parameters in nonlinear systems

Andrew D. Wilson; Todd D. Murphey

doi:10.1109/ACC.2014.6858649

Local E-optimality conditions for trajectory design to estimate parameters in nonlinear systems

Andrew D. Wilson, Todd D. Murphey

Mechanical Engineering

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

3 Scopus citations

Abstract

This paper develops an optimization method to synthesize trajectories for use in the identification of system parameters. Using widely studied techniques to compute Fisher information based on observations of nonlinear dynamical systems, an infinite-dimensional, projection-based optimization algorithm is formulated to optimize the system trajectory using eigenvalues of the Fisher information matrix as the cost metric. An example of a cart-pendulum simulation demonstrates a significant increase in the Fisher information using the optimized trajectory with decreased parameter variances shown through Monte-Carlo tests and computation of the Cramer-Rao lower bound.

Original language	English (US)
Title of host publication	2014 American Control Conference, ACC 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	443-450
Number of pages	8
ISBN (Print)	9781479932726
DOIs	https://doi.org/10.1109/ACC.2014.6858649
State	Published - 2014
Event	2014 American Control Conference, ACC 2014 - Portland, OR, United States Duration: Jun 4 2014 → Jun 6 2014

Publication series

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

Other

Other	2014 American Control Conference, ACC 2014
Country	United States
City	Portland, OR
Period	6/4/14 → 6/6/14

Keywords

Estimation
Nonlinear systems
Optimal control

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/ACC.2014.6858649

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Cite this

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title = "Local E-optimality conditions for trajectory design to estimate parameters in nonlinear systems",

abstract = "This paper develops an optimization method to synthesize trajectories for use in the identification of system parameters. Using widely studied techniques to compute Fisher information based on observations of nonlinear dynamical systems, an infinite-dimensional, projection-based optimization algorithm is formulated to optimize the system trajectory using eigenvalues of the Fisher information matrix as the cost metric. An example of a cart-pendulum simulation demonstrates a significant increase in the Fisher information using the optimized trajectory with decreased parameter variances shown through Monte-Carlo tests and computation of the Cramer-Rao lower bound.",

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Wilson, AD & Murphey, TD 2014, Local E-optimality conditions for trajectory design to estimate parameters in nonlinear systems. in 2014 American Control Conference, ACC 2014., 6858649, Proceedings of the American Control Conference, Institute of Electrical and Electronics Engineers Inc., pp. 443-450, 2014 American Control Conference, ACC 2014, Portland, OR, United States, 6/4/14. https://doi.org/10.1109/ACC.2014.6858649

Local E-optimality conditions for trajectory design to estimate parameters in nonlinear systems. / Wilson, Andrew D.; Murphey, Todd D.

2014 American Control Conference, ACC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 443-450 6858649 (Proceedings of the American Control Conference).

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

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AB - This paper develops an optimization method to synthesize trajectories for use in the identification of system parameters. Using widely studied techniques to compute Fisher information based on observations of nonlinear dynamical systems, an infinite-dimensional, projection-based optimization algorithm is formulated to optimize the system trajectory using eigenvalues of the Fisher information matrix as the cost metric. An example of a cart-pendulum simulation demonstrates a significant increase in the Fisher information using the optimized trajectory with decreased parameter variances shown through Monte-Carlo tests and computation of the Cramer-Rao lower bound.

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