Title
Multitarget Tracking for Multiple Lagrangian Plants With Input-to-Output Redundancy and Sampled-Data Interactions
Abstract
This article investigates the multitarget tracking problem for multiple Lagrangian plants (MLPs) in the presence of sampled-data interactions, uncertain dynamic terms, and input-to-output redundancy. Two classes of impulsive estimator-based control (IEC) algorithms, including the first- and higher-order IEC algorithms, are newly designed to observe the dynamic uncertain terms, estimate the states of the multiple targets, and finally solve the above-mentioned problem. Based on the properties of the small-value norms, Lyapunov stability theory, Schur stability theory, and Hurwitz criterion, some sufficient conditions and the convergence radius are derived for guaranteeing the convergence of these IEC algorithms. Finally, numerical simulations are performed on networked heterogeneous manipulators to verify the effectiveness of the proposed algorithms.
Year
DOI
Venue
2022
10.1109/TSMC.2021.3129823
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Keywords
DocType
Volume
Impulsive estimator-based control (IEC) algorithm,input-to-output redundancy,multiple Lagrangian plants (MLPs),multitarget tracking
Journal
52
Issue
ISSN
Citations 
9
2168-2216
1
PageRank 
References 
Authors
0.35
29
5
Name
Order
Citations
PageRank
Chang-Duo Liang121.03
Ming-Feng Ge2659.85
Zhi-Wei Liu334315.04
Yan-Wu Wang451039.98
Bo Li557845.93