This article investigates the neural-network-based adaptive predefined-time tracking control problem for switched nonlinear systems. Neural networks are employed to approximate the unknown part of nonlinear functions. The finite-time differentiators are introduced to estimate the first derivative of the virtual controllers. Then, a novel adaptive predefined-time controller is proposed by utilizing the backstepping control technique and the common Lyapunov function (CLF) method. It is explained by the theoretical analysis that the developed controller guarantees that all signals of the switched closed-loop systems are bounded under arbitrary switchings and the tracking error converges to zero within the predefined time. A simulation is shown to verify the validity of the developed predefined-time control approach.

中文翻译：

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切换非线性系统的预定义时间自适应神经跟踪控制


本文研究了切换非线性系统的基于神经网络的自适应预定义时间跟踪控制问题。神经网络用于逼近非线性函数的未知部分。引入有限时间微分器来估计虚拟控制器的一阶导数。然后，利用反步控制技术和通用李亚普诺夫函数（CLF）方法提出了一种新型自适应预定义时间控制器。理论分析表明，所开发的控制器保证了切换闭环系统的所有信号在任意切换下都有界，并且跟踪误差在预定时间内收敛到零。仿真验证了所开发的预定义时间控制方法的有效性。