Robust H∞ filtering for networked stochastic systems with randomly occurring sensor nonlinearities and packet dropouts

In this paper, the robust H∞H∞ filtering problem is investigated for networked stochastic systems with norm bounded uncertainties and imperfect multiple transmitted measurements. The considered imperfect measurements contain randomly occurring sensor nonlinearities and packet dropouts, which are represented by multiple independent Markov chains with partially unknown transition probabilities. A one to one mapping is constructed to map the multiple independent Markov chains to an augmented one for facilitating the resultant system analysis. A sufficient condition is established to guarantee the exponential mean-square stability with fast decay rate and a certain H∞H∞ performance level of the filtering error systems. Then, the parameters of the full-order filter are expressed in terms of linear matrix inequalities (LMIs). Finally, a numerical example is shown to demonstrate the effectiveness of the proposed method.

► A exponentially mean-square stable H∞H∞ filter is proposed.
► Measurements with random sensor nonlinearities and packet dropouts are considered.
► The m independent Markov chains are introduced to describe the m measurements.
► The m independent Markov chains are mapped to an augmented one.