Distributed robust filtering with H∞ consensus of estimates

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∞ level of disagreement of estimates in a network of filters. It involves solving a convex optimization/feasibi...

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Veröffentlicht in:Automatica (Oxford) Jg. 47; H. 1; S. 1 - 13
1. Verfasser: Ugrinovskii, V.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Kidlington Elsevier Ltd 01.01.2011
Elsevier
Schlagworte:
Applied sciences
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Consensus
Control theory. Systems
Distributed robust filtering
Exact sciences and technology
Gradient algorithms
Large-scale systems
Modelling and identification
Software
Vector dissipativity
Vector Lyapunov functions
Gradient algorithms
Vector dissipativity
Large-scale systems
Distributed robust filtering
Consensus
Vector Lyapunov functions
Convex programming
Gradient descent
Hamiltonian graph
Observer
Linear matrix inequality
System identification
Mathematical programming
Large scale system
Gradient
H infinite control
Distributed system
Synchronization
Search algorithm
Robust control
Filter
Dissipative system
Descent method
Feasibility
Sensor array
Lyapunov function
ISSN:0005-1098, 1873-2836
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Beschreibung
Zusammenfassung: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∞ level of disagreement of estimates in a network of filters. It involves solving a convex optimization/feasibility problem subject to 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.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2010.10.002