Skill based control by using fuzzy neural network for hierarchical intelligent control

A novel architecture of an intelligent control system for robotic manipulators is presented. The system is an integrated approach of neuromorphic and symbolic control of a robotic manipulator, including an applied neural network for the servo control, a knowledge-based approximation, and a fuzzy neural network (FNN) for skill-based control. The neural network in the servo control level is the numerical manipulation, while the knowledge-based part is the symbolic manipulation. In neuromorphic control, the neural network compensates for the nonlinearity of the system and the uncertainty in the environment. The knowledge-based part develops the control strategy symbolically for the servo level. The FNN is used between the servo control level and the knowledge-based part to link numerals to symbols and express human skills through learning. This system is analogous to the human cerebral control structure combined with reflex action