Hierarchical Event-Triggered Learning for Cyclically Excited Systems With Application to Wireless Sensor Networks

Beuchert J, Solowjow F, Raisch J, Trimpe S, Seel T (2020)

Publication Type: Journal article

Publication year: 2020

Journal

IEEE Control Systems Letters Institute of Electrical and Electronics Engineers (IEEE)

Book Volume: 4

Pages Range: 103-108

Article Number: 8734095

Journal Issue: 1

DOI: 10.1109/LCSYS.2019.2922005

Abstract

Communication load is a limiting factor in many real-time systems. Event-triggered state estimation and event-triggered learning (ETL) methods reduce network communication by sending information only when it cannot be adequately predicted based on previously transmitted data. This letter proposes an ETL approach for nonlinear discrete-time systems with cyclic excitation. The method automatically recognizes cyclic patterns in data-even when they change repeatedly-and reduces communication load whenever the current data can be accurately predicted from previous cycles. Nonetheless, a bounded error between original and received signal is guaranteed. The cyclic excitation model, which is used for predictions, is updated hierarchically, i.e., a full model update is only performed if updating a small number of model parameters is not sufficient. A nonparametric statistical test enforces that model updates happen only if the cyclic excitation changed with high probability. The effectiveness of the proposed methods is demonstrated using the application example of wireless real-time pitch angle measurements of a human foot in a feedback-controlled neuroprosthesis. The experimental results show that communication load can be reduced by 70% while the root-mean-square error between measured and received angle is less than 1°.

Authors with CRIS profile

Thomas Seel

Involved external institutions

Technische Universität Berlin DE Germany (DE) Max Planck Institute for Intelligent Systems (MPI-IS) / Max-Planck-Institut für Intelligente Systeme DE Germany (DE)

How to cite

APA:

Beuchert, J., Solowjow, F., Raisch, J., Trimpe, S., & Seel, T. (2020). Hierarchical Event-Triggered Learning for Cyclically Excited Systems With Application to Wireless Sensor Networks. IEEE Control Systems Letters, 4(1), 103-108. https://doi.org/10.1109/LCSYS.2019.2922005

MLA:

Beuchert, Jonas, et al. "Hierarchical Event-Triggered Learning for Cyclically Excited Systems With Application to Wireless Sensor Networks." IEEE Control Systems Letters 4.1 (2020): 103-108.

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