弧焊机器人焊缝跟踪神经网络控制器

介绍了一种能提高高弧焊机器人焊缝跟踪精度的神经网络控制器，通过神经网络的补偿作用，弥补了由于无法知道机器人精确模型所造成的控制上的误差，不同于机器人控制中传统的网络控制器，本文提出并应用了基于笛卡尔空间轨迹控制的机器人焊缝跟踪神经网络，大大简化了控制算法，计算机模拟及实验表明，该控制器非常适用于只的实际焊接，对于现有机器人，无须改变其控制器内部结构，即可应用该技术，与常用的机器人关节力矩控制法相比

Application of Neural Network Controller in the Seam Tracking of Arc-Welding Robot

A neural network(NN) controller which improves the accuracy of seam tracking of an arc-welding robot is presented in this paper. The improvement of tracking accuracy can be achieved by applying the NN controller for compensating for model uncertainties of robot manipulator. Unlike the traditional NN compensation of model uncertainties which was carried through by modifying the joint/force of the robot, the proposed NN compensation is used to modify the reference Cartesian seam trajectory, which is easily applied in practice. The required internal signal level of proposed NN for the seam modification is much smaller. Simulations and experiments have been performed on an actual arc-welding robot manipulator to test the effectiveness of NN control scheme. It has been found that NN can generate better tracking performance than the traditional computed torque(CT) control method which is based on the manipulator dynamics only. One goal of this paper is to stimulate further discussion of application of NN in the arc-welding robot control.