An Efficient Method for the Joint Estimation of System Parameters and Noise Covariances for Linear Time-Variant Systems
Abstract
We present an optimization-based method for the joint estimation of system parameters and noise covariances of linear time-variant systems. Given measured data, this method maximizes the likelihood of the parameters. We solve the optimization problem of interest via a novel structure-exploiting solver. We present the advantages of the proposed approach over commonly used methods in the framework of Moving Horizon Estimation. Finally, we show the performance of the method through numerical simulations on a realistic example of a thermal system. In this example, the method can successfully estimate the model parameters in a short computational time.
Keywords
Cite
@article{arxiv.2211.12302,
title = {An Efficient Method for the Joint Estimation of System Parameters and Noise Covariances for Linear Time-Variant Systems},
author = {Léo Simpson and Andrea Ghezzi and Jonas Asprion and Moritz Diehl},
journal= {arXiv preprint arXiv:2211.12302},
year = {2023}
}
Comments
Submitted to IEEE Control Systems Letters (L-CSS) in March 2023. Contains 6 pages including 5 figures