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Arabian Journal for Science and Engineering

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30-10-2023 | Research Article-Systems Engineering

Identification of Systems Having Unstable Dynamics and Time Delays Using Delayed Recurrent Neural Networks

Authors: Sudeep Sharma, S. V. S. Prasad, T. S. Arulananth

Published in: Arabian Journal for Science and Engineering | Issue 5/2024

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Abstract

This article addresses the challenging problem of identifying unstable system dynamics with time delay. In the proposed novel scheme, a recurrent neural network (RNN) with parallel delayed architecture in closed-loop has been employed, which is referred to as closed-loop-delayed-RNN (CLDRNN). The systematic mathematical formulation is done in easy to follow steps to calculate all the parameters of unstable delayed process models. Interestingly, in proposed algorithm, all model parameters are directly estimated in terms of the optimized CLDRNN weights only, without using any prior knowledge about the unknown process dynamics. The Lyapunov theory is incorporated to get efficient learning, and an accurate condition is derived to achieve guaranteed global convergence of the proposed algorithm. Various identification experiments are conducted on benchmark unstable process examples to show the efficacy of the proposed approach.

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Title: Identification of Systems Having Unstable Dynamics and Time Delays Using Delayed Recurrent Neural Networks
Authors: Sudeep Sharma
S. V. S. Prasad
T. S. Arulananth
Publication date: 30-10-2023
Publisher: Springer Berlin Heidelberg
Published in: Arabian Journal for Science and Engineering / Issue 5/2024
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-023-08356-w

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