Linear Time-Varying Parameter Estimation

Maximum A Posteriori Approach via Semidefinite Programming

Journal article (2024)

Authors

S. Vakili Team Manuel Mazo Jr - Mechanical, Maritime and Materials Engineering
M. Khosravi Team Khosravi - Mechanical, Maritime and Materials Engineering
P. Mohajerin Esfahani Team Peyman Mohajerin Esfahani - Mechanical, Maritime and Materials Engineering
M. Mazo Team Manuel Mazo Jr - Mechanical, Maritime and Materials Engineering
Research Group
Team Manuel Mazo Jr (Mechanical, Maritime and Materials Engineering) (TU Delft)
Copyright: © 2024 S. Vakili, M. Khosravi, P. Mohajerin Esfahani, M. Mazo
DOI
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https://doi.org/10.1109/LCSYS.2023.3347198
Optimization Estimation Semidefinite programming Identification Linear matrix inequalities
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Published Date

2024

Language

English

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Delft Center for Systems and Control

Research Group

Team Manuel Mazo Jr

Abstract

We study the problem of identifying a linear time-varying output map from measurements and linear time-varying system states, which are perturbed with Gaussian observation noise and process uncertainty, respectively. Employing a stochastic model as prior knowledge for the parameters of the unknown output map, we reconstruct their estimates from input/output pairs via a Bayesian approach to optimize the posterior probability density of the output map parameters. The resulting problem is a non-convex optimization, for which we propose a tractable linear matrix inequalities approximation to warm-start a first-order subsequent method. The efficacy of our algorithm is shown experimentally against classical Expectation Maximization and Dual Kalman Smoother approaches.