Delay and Packet-Drop Tolerant Multistage Distributed Average Tracking in Mean Square

This article studies the distributed average tracking (DAT) problem pertaining to a discrete-time linear time-invariant multiagent network, which is subject to, concurrently, input delays, random packet drops, and reference noise. The problem amounts to an integrated design of delay and a packet-dro...

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Vydáno v:IEEE transactions on cybernetics Ročník 52; číslo 9; s. 9535 - 9545
Hlavní autoři: Chen, Fei, Chen, Changjiang, Guo, Ge, Hua, Changchun, Chen, Guanrong
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.09.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Convergence
Delays
Distributed average tracking (DAT)
Eigenvalues
Heuristic algorithms
input delay
Kalman filters
multiagent system
Multiagent systems
Network topology
packet drop
Prediction algorithms
Predictive control
reference noise
Reference signals
Robustness
Robustness (mathematics)
Topology
Tracking errors
Upper bounds
ISSN:2168-2267, 2168-2275, 2168-2275
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Shrnutí:This article studies the distributed average tracking (DAT) problem pertaining to a discrete-time linear time-invariant multiagent network, which is subject to, concurrently, input delays, random packet drops, and reference noise. The problem amounts to an integrated design of delay and a 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, multistage consensus filtering, and predictive control is employed, which gives rise to a simple yet comepelling DAT algorithm that is robust to the initialization error and allows the tradeoff 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.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2021.3062035