Oh S Y, Shin W C, Kim H G
Department of Electrical Engineering, Pohang University of Science and Technology, South Korea.
Int J Neural Syst. 1995 Sep;6(3):257-71. doi: 10.1142/s0129065795000196.
The industrial robot's dynamic performance is frequently measured by positioning accuracy at high speeds and a good dynamic controller is essential that can accurately compute robot dynamics at a servo rate high enough to ensure system stability. A real-time dynamic controller for an industrial robot is developed here using neural networks. First, an efficient time-selectable hidden layer architecture has been developed based on system dynamics localized in time, which lends itself to real-time learning and control along with enhanced mapping accuracy. Second, the neural network architecture has also been specially tuned to accommodate servo dynamics. This not only facilitates the system design through reduced sensing requirements for the controller but also enhances the control performance over the control architecture neglecting servo dynamics. Experimental results demonstrate the controller's excellent learning and control performances compared with a conventional controller and thus has good potential for practical use in industrial robots.
工业机器人的动态性能通常通过高速定位精度来衡量，并且一个良好的动态控制器至关重要，它能够以足够高的伺服速率精确计算机器人动力学，以确保系统稳定性。本文利用神经网络开发了一种用于工业机器人的实时动态控制器。首先，基于时间局部化的系统动力学开发了一种高效的时间可选隐藏层架构，它有助于实时学习和控制，并提高映射精度。其次，神经网络架构也经过了专门调整以适应伺服动力学。这不仅通过降低控制器的传感要求来促进系统设计，而且与忽略伺服动力学的控制架构相比，还提高了控制性能。实验结果表明，与传统控制器相比，该控制器具有出色的学习和控制性能，因此在工业机器人中具有良好的实际应用潜力。
