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Distributed robust filtering with $H_\infty$ consensus of estimates. (English) Zbl 1209.93152
Summary: The paper addresses a problem of design of distributed robust filters using the recent vector dissipativity theory. The main result is a sufficient condition which guarantees a suboptimal $H_\infty$ level of disagreement of estimates in a network of filters. It involves solving a convex optimization/feasibility problem subject to Linear Matrix Inequality (LMI) constraints. The special case of balanced interconnection graphs is also considered. A gradient descent type algorithm is presented which allows the nodes to compute their estimator parameters in a decentralized manner. The proposed approach is applied to the problem of observer-based robust synchronization of a nonlinear network to an isolated node.

93E11Filtering in stochastic control
93B51Design techniques in systems theory
93A15Large scale systems
Full Text: DOI
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