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

• 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
• ISSN: 2168-2216; 2168-2232
• DOI: 10.1109/TSMC.2021.3129823

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.