Parameter and State Estimation of Nonlinear Systems Using a Multi-Observer under the Supervisory Framework
Loading...
Download
Associated Records
Authors
File Licence
Editors
Date
2015-09-01
Source Title
IEEE Transactions on Automatic Control
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Melbourne Authors
Affiliation
Electrical and Electronic Engineering
Medicine (St Vincent's)
Medicine (St Vincent's)
Document Type
Journal Article
Subject Code
Citation
Chong, M. S., Nesic, D., Postoyan, R. & Kuhlmann, L. (2015). Parameter and State Estimation of Nonlinear Systems Using a Multi-Observer under the Supervisory Framework. IEEE Transactions on Automatic Control, 60 (9), pp.2336-2349. https://doi.org/10.1109/TAC.2015.2406978.
Access Status
Open Access
Access URL
Open Access URL
Open Access at PMC
Linked Resource URL
NHMRC Grant Code
ARC Grant Code
Description
Abstract
We present a hybrid scheme for the parameter and state estimation of nonlinear continuous-time systems, which is inspired by the supervisory setup used for control. State observers are synthesized for some nominal parameter values and a criterion is designed to select one of these observers at any given time instant, which provides state and parameter estimates. Assuming that a persistency of excitation condition holds, the convergence of the parameter and state estimation errors to zero is ensured up to a margin, which can be made as small as desired by increasing the number of observers. To reduce the potential computational complexity of the scheme, we explain how the sampling of the parameter set can be dynamically updated using a zoom-in procedure. This strategy typically requires a fewer number of observers for a given estimation error margin compared to the static sampling policy. The results are shown to be applicable to linear systems and to a class of nonlinear systems. We illustrate the applicability of the approach by estimating the synaptic gains and the mean membrane potentials of a neural mass model.