Optimal H∞ filtering for nonlinear delayed systems with multiple sensors

We investigate the problem of multi-sensor optimal H∞ filtering for a class of nonlinear systems with time delays. We use a nonlinear system model consisting of a linear dynamic system and a bounded static nonlinear operator. It unifies some (delayed) intelligent systems, including neural networks, Takagi and Sugeno (T-S) fuzzy models, Lur'e systems, as well as linear systems. Based on the H∞ performance analysis of this nonlinear model via linear matrix inequality, we design centralized and distributed filters for multi-sensor time-delayed systems to guarantee the asymptotic stability of the fusion error systems and minimize the effect of the noise signals on the filtering error, and compare the effectiveness of centralized and distributed filters. We obtain parameters of these filters by solving an eigenvalue problem (EVP). A simulation example is provided to illustrate the design procedure and performance.

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