Resilient H∞$$ {H}_{\infty } $$ filtering for discrete‐time system with multipath data packet dropout and quantization

Abstract

This paper proposes a two-step method to design a resilient filter and two dynamic quantizers for a class of discrete-time systems. In the first step, a filter is designed to make the discrete-time system stochastically stable, and make the H ∞ norm between the filtering error and the external disturbance less than a given level. In the second step, two dynamic quantizers, which quantify the measurement output signal and the performance output signal, respectively, are designed under the condition that the filter gain matrices are obtained. In addition, the filter adopted in this paper takes the randomly occurring uncertainty into consideration, and the quantizers are designed by an inequality scaling technique which can reduce the complexity of linear matrix inequalities. Furthermore, this paper also considers the data packet dropout that may occur during network transmission. Finally, considering the data packet dropout and quantization of measurement output signal and performance output signal, the effectiveness of the proposed filter design method is demonstrated by two simulation examples.