Journal
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
Volume 25, Issue 13, Pages 1941-1966Publisher
WILEY
DOI: 10.1002/rnc.3178
Keywords
dynamic average consensus; time-varying input signals; directed graphs; rate of convergence; limited control authority; privacy preservation
Funding
- L3 Communications through the UCSD Cymer Center for Control Systems and Dynamics
- NSF award [CMMI-1300272]
- AFOSR award [11RSL548]
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This paper introduces a novel continuous-time dynamic average consensus algorithm for networks whose interaction is described by a strongly connected and weight-balanced directed graph. The proposed distributed algorithm allows agents to track the average of their dynamic inputs with some steady-state error whose size can be controlled using a design parameter. This steady-state error vanishes for special classes of input signals. We analyze the asymptotic correctness of the algorithm under time-varying interaction topologies and characterize the requirements on the stepsize for discrete-time implementations. We show that our algorithm naturally preserves the privacy of the local input of each agent. Building on this analysis, we synthesize an extension of the algorithm that allows individual agents to control their own rate of convergence towards agreement and handle saturation bounds on the driving command. Finally, we show that the proposed extension additionally preserves the privacy of the transient response of the agreement states and the final agreement value from internal and external adversaries. Numerical examples illustrate the results. Copyright (C) 2014 John Wiley & Sons, Ltd.
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