This paper concentrates on asymmetric barrier Lyapunov functions ( ABLFs ) based on finite-time adaptive neural network ( NN ) control methods for a class of nonlinear strict feedback systems with time-varying full state constraints. During the process of backstepping recursion, the approximation properties of NNs are exploited to address the problem of unknown internal dynamics. The ABLFs are constructed to make sure that the time-varying asymmetrical full state constraints are always satisfied. According to the Lyapunov stability and finite-time stability theory, it is proven that all the signals in the closed-loop systems are uniformly ultimately bounded ( UUB ) and the system output is driven to track the desired signal as quickly as possible near the origin. In the meantime, in the scope of finite-time, all states are guaranteed to stay in the pre-given range. Finally, a simulation example is proposed to verify the feasibility of the developed finite time control algorithm.

中文翻译：

---

基于时变非对称BLF的具有全状态约束的非线性系统的自适应有限时间神经控制

本文针对具有时变全状态约束的一类非线性严格反馈系统，基于有限时间自适应神经网络（NN）控制方法，着重研究了非对称势垒Lyapunov函数（ABLF）。在递推递归过程中，利用神经网络的近似特性来解决未知内部动力学问题。ABLF的构造可确保始终满足时变的非对称全状态约束。根据Lyapunov稳定性和有限时间稳定性理论，证明了闭环系统中的所有信号均受到统一的最终有界（UUB），并且驱动系统输出以在原点附近尽快跟踪所需信号。 。同时，在有限时间范围内，保证所有州都在预定范围内。最后，通过仿真实例验证了所开发的有限时间控制算法的可行性。