Distributed event-triggered fusion estimation for networked asynchronous multi-rate systems

This paper studies the event-triggered distributed fusion estimation problem for a class of networked asynchronous multi-rate systems with nonlinear disturbances and bounded noises. Firstly, the asynchronous multi-rate systems are transformed into synchronous ones at the measurement sampling points (MSPs) by iterating the state equation. This paper proposes an event-triggered strategy to alleviate the communication burden to meet the finite communication resources during information transmission. Since the reduction of information will inevitably reduce the estimation performance, a compensation mechanism is adopted to restructure the untransmitted measurement. Then, a local estimator at the MSP is designed by the compensation information, and the local estimation at the unsampled measurement point is obtained by the multi-step prediction. Furthermore, by constructing the upper bound of the fusion estimation error, a robust recursive optimization approach, which can deal with nonlinear disturbances and bounded noises, is proposed to determine the distributed fusion criterion. Meanwhile, the stability conditions are derived such that the square errors of the designed estimators are asymptotically bounded. Finally, a target tracking example is used to demonstrate the effectiveness of the proposed method.