2 research outputs found
Distributed Robust Dynamic Average Consensus with Dynamic Event-Triggered Communication
This paper presents the formulation and analysis of a fully distributed
dynamic event-triggered communication based robust dynamic average consensus
algorithm. Dynamic average consensus problem involves a networked set of agents
estimating the time-varying average of dynamic reference signals locally
available to individual agents. We propose an asymptotically stable solution to
the dynamic average consensus problem that is robust to network disruptions.
Since this robust algorithm requires continuous communication among agents, we
introduce a novel dynamic event-triggered communication scheme to reduce the
overall inter-agent communications. It is shown that the event-triggered
algorithm is asymptotically stable and free of Zeno behavior. Numerical
simulations are provided to illustrate the effectiveness of the proposed
algorithm
Delay and Packet-Drop Tolerant Multi-Stage Distributed Average Tracking in Mean Square
This paper studies the distributed average tracking problem pertaining to a
discrete-time linear time-invariant multi-agent network, which is subject to,
concurrently, input delays, random packet-drops, and reference noise. The
problem amounts to an integrated design of delay and packet-drop tolerant
algorithm and determining the ultimate upper bound of the tracking error
between agents' states and the average of the reference signals. The
investigation is driven by the goal of devising a practically more attainable
average tracking algorithm, thereby extending the existing work in the
literature which largely ignored the aforementioned uncertainties. For this
purpose, a blend of techniques from Kalman filtering, multi-stage consensus
filtering, and predictive control is employed, which gives rise to a simple yet
comepelling distributed average tracking algorithm that is robust to
initialization error and allows the trade-off between communication/computation
cost and stationary-state tracking error. Due to the inherent coupling among
different control components, convergence analysis is significantly
challenging. Nevertheless, it is revealed that the allowable values of the
algorithm parameters rely upon the maximal degree of an expected network, while
the convergence speed depends upon the second smallest eigenvalue of the same
network's topology. The effectiveness of the theoretical results is verified by
a numerical example