https://doi.org/10.1140/epjs/s11734-024-01335-8
Regular Article
State estimation for nonlinear systems using a recurrent neural network learning algorithm and an event-triggered state observer
1
Faculty of Management Information Systems, University of Finance and Accountancy, No. 02 Le Quy Don, Tu Nghia District, 570000, La Ha, Quang Ngai, Vietnam
2
Faculty of Automotive Engineering Technology, Industrial University of Ho Chi Minh City, No. 12 Nguyen Van Bao, 70000, Ward 4, Go Vap District, Ho Chi Minh, Vietnam
Received:
25
April
2024
Accepted:
7
September
2024
Published online:
9
October
2024
In this paper, we propose a novel method to estimate the states of nonlinear systems. A recurrent neural network learning algorithm is first developed to predict the nonlinear systems. Then, an event-triggered state observer is designed for the recurrent neural network. This state observer robustly estimates state vTariables of the nonlinear systems. A sufficient condition in terms of a convex optimization problem for the existence of the event-triggered state observer is established. In contrast with the abundance of state estimation methods based on time-triggered state observers where the measurements are always continuously available, the ones in this paper are updated when an event-triggered condition holds. Therefore, it lessens the stress on communication resources while still maintaining an estimation performance. The obtained theoretical analysis is applied to estimate the electrical angular velocity, the electrical angle, and the currents of the permanent magnet synchronous motor.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
