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Robot Manipulators Control with Guaranteed Stability Using Feedback Error Learning Neural Networks
Ju-Jang Lee, Sung-Woo Kim, and Kang-Bark Park
Department of Electrical Engineering Korea Advanced Institute of Science and Technology, 373-1 Kusong-Dong, Yusong-Gu, Taejon 305-701, Korea
Received:April 8, 1996Accepted:May 10, 1996Published:August 20, 1996
Keywords:Neural networks, Feedback error learning, Robot manipulator control, Guaranteed stability
Abstract
Among various neural network learning control schemes, feedback error learning(FEL)8),9) has been known that it has advantages over other schemes. However, such advantages are founded on the assumption that the systems is linearly parameterized and stable. Thus, FEL has difficulties in coping with uncertain and unstable systems. Furthermore, it is not clear how the learning rule of FEL is obtained in the minimization sense. Therefore, to overcome such problems, we propose neural network control schemes using FEL with guaranteed performance. The proposed strategy is to use multi-layer neural networks, to design a stabilityguaranteeing controller(SGC), and to derive a learning rule to obtain the tracking performance. Using multilayer neural networks we can fully utilize the learning capability no matter how the system is linearly parameterized or not. The SGC makes it possible for the neural network to learn without fear of instability. As a result, the more the neural network learning proceeds, the better the tracking performance becomes.
Cite this article as:J. Lee, S. Kim, and K. Park, “Robot Manipulators Control with Guaranteed Stability Using Feedback Error Learning Neural Networks,” J. Robot. Mechatron., Vol.8 No.4, pp. 383-391, 1996.Data files:
