Neural network based dynamic controllers for industrial robots.

S Y Oh; W C Shin; H G Kim

doi:10.1142/s0129065795000196

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Neural network based dynamic controllers for industrial robots.

S Y Oh·W C Shin·H G Kim

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Abstract

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.

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Algorithms Artificial Intelligence Computer Systems Industry Neural Networks, Computer Robotics Journal Article Research Support, Non-U.S. Gov't

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S Y Oh, W C Shin, H G Kim. 1995. Neural network based dynamic controllers for industrial robots.. https://doi.org/10.1142/s0129065795000196

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