Network-Based Data-Driven Filtering with Bounded Noises and Packet Dropouts

This paper is concerned with the problem of a network-based data-driven filter design for discrete-time linear systems with bounded noises and packet dropouts. One favorable feature is that the designed filter can be directly employed without identifying the unknown system model. To compensate the negative effects of packet dropouts, an output predictor is first designed to reconstruct the missing data based on the received outputs and the inputs of the system. The asymptotic convergence of the output prediction error is established, of which the rate can be adjusted by the parameter. Then utilizing the predicted outputs and the received measurements, an almost-optimal data-driven filter with tractability is proposed within the set membership (SM) framework and the bound on the worst case estimation error is derived. Finally, two illustrative examples, including a comparison example and an application example, are presented to show the advantages of the proposed design and the effectiveness of the theoretical results.