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Neural Network-Based Tracking Control of Uncertain Robotic Systems: Predefined-Time Nonsingular Terminal Sliding-Mode Approach
IEEE Transactions on Industrial Electronics ( IF 7.5 ) Pub Date : 2022-03-29 , DOI: 10.1109/tie.2022.3161810
Yizhuo Sun 1 , Yabin Gao 1 , Yue Zhao 1 , Zhuang Liu 1 , Jiahui Wang 2 , Jiyuan Kuang 1 , Fei Yan 3 , Jianxing Liu 1
Affiliation  

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:


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。
更新日期:2022-03-29
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