Multitarget Tracking for Multiple Lagrangian Plants With Input-to-Output Redundancy and Sampled-Data Interactions
针对存在采样数据交互、不确定动态项和输入输出冗余的多拉格朗日对象，设计了两种脉冲估计器控制算法，并给出收敛条件，仿真验证了有效性。
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.