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首页> 外文期刊>International Journal of Precision Engineering and Manufacturing >A novel adaptive finite-time tracking control for robotic manipulators using nonsingular terminal sliding mode and RBF neural networks
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A novel adaptive finite-time tracking control for robotic manipulators using nonsingular terminal sliding mode and RBF neural networks

机译：基于非奇异终端滑模和RBF神经网络的机器人机械臂自适应有限时间跟踪控制

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摘要

This paper presents a novel adaptive terminal sliding mode controller for the trajectory tracking of robotic manipulators using radial basis function neural networks (RBFNNs). First, a modified terminal sliding mode (TSM) surface is approached to avoid the singularity problem of conventional TSM. Then, a nonsingular TSM control is designed for joint position tracking of a robotic manipulator. In the control scheme, fully tuned RBFNNs are adopted to approximate the nonlinear unknown dynamics of the robotic manipulator. Adaptive learning algorithms are derived to allow online adjustment of the output weights, the centers and the variances in the RBFNNs. Meanwhile, a continuous robust control term is added to eliminate chattering efforts in the sliding mode control (SMC) system. The stability and finite-time convergence of the closed-loop system are established by using Lyapunov theory. Finally, the simulation results of a two-link robotic manipulator are presented to demonstrate the effectiveness of the proposed control method.
机译：本文提出了一种新颖的自适应终端滑模控制器，该控制器利用径向基函数神经网络（RBFNN）跟踪机器人的轨迹。首先，采用改进的终端滑模（TSM）表面来避免常规TSM的奇异性问题。然后，设计了一个非奇异的TSM控件来跟踪机器人操纵器的关节位置。在控制方案中，采用完全调谐的RBFNN来近似机器人机械手的非线性未知动力学。导出了自适应学习算法，以允许在线调整RBFNN中的输出权重，中心和方差。同时，添加了连续的鲁棒控制项，以消除滑模控制（SMC）系统中的抖动现象。利用李雅普诺夫理论建立了闭环系统的稳定性和有限时间收敛性。最后，给出了两连杆机械手的仿真结果，以证明所提出的控制方法的有效性。

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