Hierarchical control system in intelligent robotics and mechatronics

This paper introduces a hierarchical control scheme for intelligent robotics and mechatronics. 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.<<ETX>>

[1]  Toshio Fukuda,et al.  Hierarchical intelligent control for robotic motion , 1994, IEEE Trans. Neural Networks.

[2]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[3]  Toshio Fukuda,et al.  Theory and applications of neural networks for industrial control systems , 1992, IEEE Trans. Ind. Electron..

[4]  Takanori Shibata,et al.  Fuzzy critic for robotic motion planning by genetic algorithm in hierarchical intelligent control , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[5]  C. L. Karr,et al.  Fuzzy control of pH using genetic algorithms , 1993, IEEE Trans. Fuzzy Syst..

[6]  Toshio Fukuda,et al.  Coordinative behavior by genetic algorithm and fuzzy in evolutionary multi-agent system , 1993, [1993] Proceedings IEEE International Conference on Robotics and Automation.

[7]  Toshio Fukuda,et al.  Neuromorphic control: adaptation and learning , 1992, IEEE Trans. Ind. Electron..

[8]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[9]  Kazuhiro Kosuge,et al.  Skill based control by using fuzzy neural network for hierarchical intelligent control , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[10]  Kazuhiro Kosuge,et al.  Selfish and coordinative planning for multiple mobile robots by genetic algorithm , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

[11]  Donald A. Sofge,et al.  Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches , 1992 .

[12]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[13]  Thomas Parisini,et al.  Radial basis functions and multilayer feedforward neural networks for optimal control of nonlinear stochastic systems , 1993, IEEE International Conference on Neural Networks.

[14]  Fumihito Arai,et al.  A new neuron model for additional learning , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.