Special Issue on Fuzzy Control
Professor, Department of Mechanical Engineering, Faculty of Science and Engineering, Saga University, 1-Honjo-Machi, Saga, 840 Japan
This special issue is devoted to the study of Fuzzy Control applied to robotics and mechatronics. In particular, it contains a collection of fuzzy-neural network approaches, together with the conventional fuzzy reasoning or new approaches. Since the first pioneering work on fuzzy sets and fuzzy logic reported in 1965 by Zadeh, many control application papers have been published with the fundamental fuzzy controllers based on the so-called Mamdani’s min-max centroidal method, the TakagiSugeno’s functional reasoning, and the simplified reasoning. However, it is recognized that much trial and error is necessary in the design of the conventional fuzzy controller, because the fuzzy reasoning methods mentioned above are not fundamentally related to any control or system theory. In addition, it should be noted that the total number of control rules grows exponentially as the number of input variables to the conventional fuzzy reasoning increases. Thus, in order to improve the conventional approach and develop the new approach for large-scale systems, most current work on fuzzy control is concerned with an effective design, construction, or analysis of the fuzzy controller by invoking the neural network theory, genetic algorithm, and other control or system theories. Although the literature, both in Japanese and in English, on fuzzy control and applications is now very rich, I believe that this special issue provides an important impact on the advanced fuzzy control. This issue would not have been possible without the enthusiastic support of the contributors. I am indebted to all of them for their up-to-date contributions and to the editorial staff for care throughout the editorial and printing process.