机械臂的神经滑模控制

M. Ertugrul, O. Kaynak
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引用次数: 208

摘要

本文提出了一种神经网络与滑模控制(SMC)的协同组合。消除了抖振,提高了SMC的误差性能。在这种方法中,提出了两个并行神经网络来实现SMC。等效控制和SMC的校正项是神经网络的输出。采用梯度下降法进行权值自适应。将该方法应用于SCARA型机械臂的控制,并给出了仿真结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neuro-sliding mode control of robotic manipulators
In this paper, a synergistic combination of neural networks with sliding mode control (SMC) is proposed. As a result, the chattering is eliminated and error performance of SMC is improved. In such an approach, two parallel NNs are proposed to realize SMC. The equivalent control and the corrective term of SMC are the outputs of the NNs. The gradient descent method is used for the weight adaptation. This novel approach is applied to control of a SCARA type robot manipulator and simulation results are given.
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