  Common event-triggered state estimation (ETSE) algorithms save communication
in networked control systems by predicting agents' behavior, and transmitting
updates only when the predictions deviate significantly. The effectiveness in
reducing communication thus heavily depends on the quality of the dynamics
models used to predict the agents' states or measurements. Event-triggered
learning is proposed herein as a novel concept to further reduce communication:
whenever poor communication performance is detected, an identification
experiment is triggered and an improved prediction model learned from data.
Effective learning triggers are obtained by comparing the actual communication
rate with the one that is expected based on the current model. By analyzing
statistical properties of the inter-communication times and leveraging powerful
convergence results, the proposed trigger is proven to limit learning
experiments to the necessary instants. Numerical and physical experiments
demonstrate that event-triggered learning improves robustness toward changing
environments and yields lower communication rates than common ETSE.
