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Event-Triggered Multiple-Model Identifier for a Class of Nonlinear Systems

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Abstract

This paper treats the event-triggered identification of a class of discrete-time nonlinear dynamical systems. Feedforward neural networks are trained online to behave like the given plant. With event triggering, the neural network parameters are not updated with every available data point. Instead, training takes place as and when required. We show that event-triggered identification requires only a small percentage of data points, resulting in considerably less computational requirement. In addition, we demonstrate that introducing multiple identification models in this scenario requires fewer data points with minimal degradation in the identification performance. We further show that this is better possible with the online sequential learning algorithm for networks with one or two hidden layers than the traditional back propagation algorithm.

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Acknowledgements

The authors thank the reviewers for their support through criticisms and comments which helped in improving the quality of the paper.

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Authors and Affiliations

  1. Department of ECE, PES University, 100 Feet Ring Road, BSK 3rd Stage, Bangalore, Karnataka, 560085, India

    Rashmi Ugarakhod & Shikha Tripathi

  2. Department of EECE, Gandhi Institute of Technology and Management (GITAM), Nagadenahalli, Bangalore, Karnataka, 561203, India

    Koshy George

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  1. Rashmi Ugarakhod
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  2. Shikha Tripathi
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Correspondence to Shikha Tripathi.

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Ugarakhod, R., Tripathi, S. & George, K. Event-Triggered Multiple-Model Identifier for a Class of Nonlinear Systems. J Control Autom Electr Syst 34, 971–984 (2023). https://doi.org/10.1007/s40313-023-01015-3

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