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.<<ETX>>

[1]  Madan M. Gupta,et al.  On fuzzy neuron models , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[2]  Paul J. Werbos,et al.  Neurocontrol and related techniques , 1990 .

[3]  J. Slotine,et al.  On the Adaptive Control of Robot Manipulators , 1987 .

[4]  Jens Rasmussen,et al.  Skills, rules, and knowledge; signals, signs, and symbols, and other distinctions in human performance models , 1983, IEEE Transactions on Systems, Man, and Cybernetics.

[5]  Toshio Fukuda,et al.  Research Trends in Neuromorphic Control , 1990, J. Robotics Mechatronics.

[6]  M. Kawato,et al.  Hierarchical neural network model for voluntary movement with application to robotics , 1988, IEEE Control Systems Magazine.

[7]  Kazuhiro Kosuge,et al.  New strategy for hierarchical intelligent control of robotic manipulator-hybrid neuromorphic and symbolic control , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.

[8]  Shinichi Hirai,et al.  Towards a symbolic-level force feedback: recognition of assembly process states , 1991 .

[9]  H. Harry Asada,et al.  Automatic program generation from teaching data for the hybrid control of robots , 1989, IEEE Trans. Robotics Autom..

[10]  Klaus Landzettel,et al.  Sensory feedback structures for robots with supervised learning , 1985, Proceedings. 1985 IEEE International Conference on Robotics and Automation.

[11]  T. Mitsuoka,et al.  Neural network application for robotic motion control-adaptation and learning , 1990, 1990 IJCNN International Joint Conference on Neural Networks.

[12]  Toshio Fukuda,et al.  Force control of robot manipulator by neural network. (Control of one degree-of-freedom manipulator). , 1989 .

[13]  Sheng Liu,et al.  Transfer of human skills to neural net robot controllers , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[14]  G. N. Saridis,et al.  Intelligent robotic control , 1983 .

[15]  Fumihito Arai,et al.  Adaptation and learning for hierarchical intelligent control , 1991, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks.

[16]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.