Hierarchical intelligent control for robotic motion by using fuzzy, artificial intelligence, and neural network

An intelligent-control structure for robotic motion is presented. This system is analogous to the human cerebral control structure for intelligent control. Therefore, the system has a hierarchical structure as an integrated approach of neuromorphic and symbolic control, including an applied neural network for servo control, a knowledge-based approximation, and a fuzzy set theory for a human interface. The neural network in the servo control level is responsible for numerical manipulation, while the knowledge-based part is responsible for symbolic manipulation. In the neuromorphic control, the neural network compensates for the nonlinearity of the system and uncertainty in its environment. The knowledge base part develops control strategies symbolically for the servo level with a priori knowledge. The fuzzy logic combined with the neural network is used between the servo control level and the knowledge-based part to link numerals to symbols and express human skills through learning.<
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