Event-Based Distributed $H_{\infty }$ Filtering Networks of 2-DOF Quarter-Car Suspension Systems

This paper is concerned with the problem of vertical attitude estimation of a two-degree-of-freedom quarter-car suspension system by designing a distributed filtering network, where several distributed filters estimate vehicle heave motion cooperatively under consideration of external disturbance, network channel noises, and measurement error. The sampled data are transmitted through wireless networks. In order to reduce network traffic load and save communication resources, a novel periodic event-triggered sampling scheme is proposed, under which data are transmitted only when the proposed triggering condition is violated. Codesign of event-triggered and distributed filters is derived to guarantee well $H_{\infty }$ robustness to the system noises considered above. Finally, the experiments are given to show the effectiveness of the proposed filtering system.