제어로봇시스템학회:학술대회논문집
- 제어로봇시스템학회 1989년도 한국자동제어학술회의논문집; Seoul, Korea; 27-28 Oct. 1989
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- Pages.1113-1119
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- 1989
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)
- 발행 : 1989.10.01
초록
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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