Special Issue on Intelligent Control in Coming New Generation
Shigeyasu Kawaji* and Tetsuo Sawaragi**
*Department of Systems Engineering and Information Science, Graduate School of Science and Technology, Kumamoto University, 2-39-1 Kurokami, Kumamoto 860-8555, Japan
**Department of Precision Engineering Graduate School of Engineering, Kyoto University, Yoshida-Honmachi, Kyoto 606-8501,Japan
In the early 1970s, a concept of intelligent control was proposed by Fu, and since then the advancement of control technologies as a migrate of control theory, artificial intelligence and operations research has been actively attempted. The breakthrough of this concept was to integrate a human judgment and a concept of value as well as management theory into conventional control theoretic approaches, and synthesize these as artificial intelligence. A number of unconventional control techniques have evolved, offering solutions to many difficult control problems in industry and manufacturing. Saridis proposed a general architecture for intelligent control and proposed a design principle of such a hierarchical system as the principle of Increasing Precision with Decreasing Intelligence. During the first generation of intelligent control, a number of intelligent methodologies besides the purely symbolic and logical processing of human knowledge were introduced. They are broadly called soft computing techniques that include artificial neural networks, fuzzy logic, genetic algorithm, and chaos theory. These techniques have contributed much to the advancement of intelligent control from the viewpoint of its “intelligence” part, but no solutions are provided from a control theoretic viewpoint, and the definition of intelligence in terms of control theory is still left questionable. To discuss this issue, we initiated a specialist’s meeting on survey of intelligent control in 1997 organized under the Institute of Electrical Engineers of Japan, and discussed the current status as well as future perspectives of intelligent control. Some of the papers contributed to this special issue are results obtained in this series of meetings. During that time, the framework of intelligent control has entered the second generation. In the first stage, this framework was discussed in terms of utilized methodologies such as control theory, artificial intelligence, and operations research seeking optimal combinations of these methodologies wherein a distinction is made between the controller, the plant, and the external environment and representations as well as state concepts utilized were a priorily determined and fixed without flexibility. In contrast, the second generation intelligent control system must emphasize a biologically inspired architecture that can accommodate the flexible and dynamic capabilities of living systems including human beings. That is, it must be able to grow and develop increasing capabilities of self-control, self-awareness of representation and reasoning about self and of constructing a coherent whole out of different representations. Actually, a new branch of research on artificial life and system theory of function emergence has shifted the perspectives of intelligence from conventional reductionism to a new design principle based on the concept of “emergence”. Thus, their approach is quite new in that they attempt to build models that bring together self-organizing mechanisms with evolutionary computation. Such a trend has forced us to reconsider the biological system and/or natural intelligence. In this special issue, we focus on the aspects of semiosis within a multigranular architecture and of emergent properties and techniques for human-machine and/or multiagent collaborative control systems in the coming new generation. These topics are mutually interrelated; the role of multivariable and multiresolutional quantization and clustering for designing intelligent controllers is essential for realizing the abilities to learn unknown multidimensional functions and/or for letting a joint system, which consists of an external environment, a human, and a machine, self-organize distinctive roles in a bottom-up and emerging fashion. This special issue includes papers on proposals of conceptual architecture, methodologies and reports from practical field studies on the hierarchical architecture of machines for realizing hierarchical collaboration and coordination among machine and human autonomies. We believe that these papers will lead to answers to the above questions. We sincerely thank the contributors and reviewers who made this special issue possible. Thanks also go to the editor-in-chief of the Journal of Robotics and Mechatronics, Prof. Makoto Kaneko (Hiroshima University), who provided the opportunity for editing this special issue.
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