Multitarget Tracking for Multiple Lagrangian Plants With Input-to-Output Redundancy and Sampled-Data Interactions

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-o...

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Published in	IEEE transactions on systems, man, and cybernetics. Systems Vol. 52; no. 9; pp. 5611 - 5622
Main Authors	Liang, Chang-Duo, Ge, Ming-Feng, Liu, Zhi-Wei, Wang, Yan-Wu, Li, Bo
Format	Journal Article
Language	English
Published	New York IEEE 01.09.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects	Algorithms Convergence Geology Heuristic algorithms IEC Impulsive estimator-based control (IEC) algorithm input-to-output redundancy Mechanical engineering multiple Lagrangian plants (MLPs) Multiple target tracking multitarget tracking Norms Redundancy Stability criteria Target tracking Tracking problem Vehicle dynamics
Online Access	Get full text
ISSN	2168-2216 2168-2232
DOI	10.1109/TSMC.2021.3129823

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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.
AbstractList	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.
Author	Ge, Ming-Feng Li, Bo Liang, Chang-Duo Wang, Yan-Wu Liu, Zhi-Wei
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SubjectTerms	Algorithms Convergence Geology Heuristic algorithms IEC Impulsive estimator-based control (IEC) algorithm input-to-output redundancy Mechanical engineering multiple Lagrangian plants (MLPs) Multiple target tracking multitarget tracking Norms Redundancy Stability criteria Target tracking Tracking problem Vehicle dynamics
Title	Multitarget Tracking for Multiple Lagrangian Plants With Input-to-Output Redundancy and Sampled-Data Interactions
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