Abstract-Communication load is a limiting factor in many real-time systems. Event-triggered state estimation and eventtriggered learning methods reduce network communication by sending information only when it cannot be adequately predicted based on previously transmitted data. This paper proposes an event-triggered learning approach for nonlinear discretetime 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 realtime pitch angle measurements of a human foot in a feedbackcontrolled neuroprosthesis. The experimental results show that communication load can be reduced by 70 % while the rootmean-square error between measured and received angle is less than 1 • .
REF proposes an ETL approach for cyclically excited systems.
Hierarchical Event-triggered Learning for Cyclically Excited Systems with Application to Wireless Sensor Networks
IEEE Control Systems Letters, vol. 4, no. 1, pp. 103-108, Jan. 2020
Mathematics Computer Science Engineering