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

• Published in: IEEE transactions on cybernetics Vol. 52; no. 9; pp. 9535 - 9545
• Main Authors: Chen, Fei, Chen, Changjiang, Guo, Ge, Hua, Changchun, Chen, Guanrong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: 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
• DOI: 10.1109/TCYB.2021.3062035

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.