A neural network architecture for dynamic control of robot manipulators

  • Ryu, Yeon-Sik (Department of Electrical Engineering Pohang Institute of Science and Technology) ;
  • Oh, Se-Young (Department of Electrical Engineering Pohang Institute of Science and Technology)
  • Published : 1989.10.01

Abstract

Neural network control has many innovative potentials for intelligent adaptive control. Among many, it promises real time adaption, robustness, fault tolerance, and self-learning which can be achieved with little or no system models. In this paper, a dynamic robot controller has been developed based on a backpropagation neural network. It gradually learns the robot's dynamic properties through repetitive movements being initially trained with a PD controller. Its control performance has been tested on a simulated PUMA 560 demonstrating fast learning and convergence.

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