Adaptive Neural Network Control for Robotic Manipulators With Unknown Deadzone
针对机器人操作臂的未知死区问题，提出自适应神经网络控制方法，通过两个径向基神经网络分别处理死区效应和未知动力学，并用仿真和实验验证。
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.