This article investigates the predefined time trajectory tracking control of uncertain nonlinear robotic systems. A radial basis function neural network (RBFNN) is used to estimate uncertainties in the robotic system dynamics. To avoid the singularity of terminal sliding-mode control (TSMC), a modified sliding variable is adopted. In order to realize that the tracking errors can converge to a small neighborhood of the origin in predefined time, within which the maximum convergence time can be adjusted by explicit parameters in advance, a nonsingular TSMC based on the RBFNN is proposed. Experiments on a ROKAE platform demonstrate the effectiveness and advantage of the proposed control method.

中文翻译：

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基于神经网络的不确定机器人系统跟踪控制：预定义时间非奇异终端滑模方法


本文研究了不确定非线性机器人系统的预定义时间轨迹跟踪控制。径向基函数神经网络（RBFNN）用于估计机器人系统动力学中的不确定性。为了避免终端滑模控制（TSMC）的奇异性，采用了修正的滑动变量。为了实现跟踪误差能够在预定时间内收敛到原点的小邻域，并且最大收敛时间可以通过显式参数提前调整，提出了一种基于RBFNN的非奇异TSMC。 ROKAE平台上的实验证明了所提出的控制方法的有效性和优势。