Abstract
This paper introduces some intelligent techniques, such as fuzzy theory, neural networks, genetic algorithms, and artificial intelligence, and its application for the hierarchical control system. The control system is classified into three levels: 1)learning level, 2)skill level, and 3)adaptation level. In the learning level, symbols are reasoned logically to control strategies. The skill level produces control references along with the control strategies and sensory information on environments. The adaptation level controls robots and machines while adapting to their environments which include uncertainties. For realization of these levels and connection among them, artificial intelligence, neural networks, fuzzy logic, and genetic algorithms are applied to the hierarchical control system while integrating and synthesizing themselves. To be intelligent, the hierarchical control system learns various experiences both in top-down manner and bottom-up manner. Thus, the hierarchical control scheme is effective for intelligent robotics and mechatronics.
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© 1995 Springer Fachmedien Wiesbaden
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Fukuda, T., Shimojima, K. (1995). Fuzzy-Neuro-GA based Intelligent Control. In: Lückel, J. (eds) Proceedings of the Third Conference on Mechatronics and Robotics. Vieweg+Teubner Verlag, Wiesbaden. https://doi.org/10.1007/978-3-322-91170-4_31
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DOI: https://doi.org/10.1007/978-3-322-91170-4_31
Publisher Name: Vieweg+Teubner Verlag, Wiesbaden
Print ISBN: 978-3-519-02625-9
Online ISBN: 978-3-322-91170-4
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