Fuzzy, neural network, and genetic algorithm based control system

This paper introduces a hierarchical control scheme based on a fuzzy, neural network, and a genetic algorithm for intelligent robotics. The scheme has three levels: learning level, skill level and adaptation level. The learning level manipulates symbols to reason logically for 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 these levels and to connect 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. The hierarchical control scheme is effective for intelligent robotics and mechatronics.<
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