I. Introduction
The concept of finite-time stability (FTS), originally proposed in [1], implies that the system state converges to an equilibrium point in finite time and no longer diverges thereafter. Compared with the traditional asymptotic stabilization [2], [3] and stabilization [4], better performances, such as faster convergence and stronger robustness against uncertainties can be achieved for the systems with FTS property. Therefore, the issue of finite-time control for various systems has received increasing attention over the past few decades. For instance, by adopting backstepping and power integrator techniques, several finite-time stabilization results for the lower-triangular systems [5], stochastic nonlinear systems [6], [7], and learning-based systems [8] in strict feedback form have been reported, respectively. Meanwhile, for the nonstrict feedback nonlinear systems, finite-time neural network-based control, filter decentralized control, and adaptive control have been investigated in [9], [10], and [11], respectively. Besides, the finite-time control problems for the specific systems, such as robot manipulators [12], [13], switched nonlinear systems [14], and impulsive complex networks [15] have also been extensively studied. However, note that it is difficult to achieve FTS for the systems with the parameter uncertainties and perturbation. Thus, the practical FTS (P-FTS) condition has been introduced in [16], which means that the system state trajectories converge to a sufficiently small region around the origin in finite time. Recently, some related results on P-FTS have been proposed, e.g., see [17] and references therein.