Event-triggered Learning for Resource-efficient Networked Control
This addresses resource efficiency for networked control systems, offering an incremental improvement over existing methods.
The paper tackles the problem of reducing communication in networked control systems by proposing event-triggered learning, which triggers model updates when poor performance is detected, and demonstrates that it improves robustness and yields lower communication rates than common event-triggered state estimation.
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.