This paper addresses the problem of robotic manipulators with unknown deadzone. In order to tackle the uncertainty and the unknown deadzone effect, we introduce adaptive neural network (NN) control for robotic manipulators. State-feedback control is introduced first and a high-gain observer is then designed to make the proposed control scheme more practical. One radial basis function NN (RBFNN) is used to tackle the deadzone effect, and the other RBFNN is also proposed to estimate the unknown dynamics of robot. The proposed control is then verified on a two-joint rigid manipulator via numerical simulations and experiments.

中文翻译：

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具有未知死区的机器人操纵器的自适应神经网络控制


本文解决了具有未知死区的机器人操纵器的问题。为了解决不确定性和未知的死区效应，我们引入了机器人操纵器的自适应神经网络（NN）控制。首先引入状态反馈控制，然后设计高增益观测器以使所提出的控制方案更加实用。一种径向基函数神经网络（RBFNN）用于解决死区效应，另一种径向基函数神经网络（RBFNN）也被提出来估计机器人的未知动力学。然后通过数值模拟和实验在两关节刚性机械臂上验证所提出的控制。