Special Issue on New Development in Adaptive & Learning Control
Shoichiro Fujisawa, Toru Yamamoto, Ikuro Mizumoto, and Tomohiro Henmi
Professor, Graduate School of Science and Technology, Tokushima University
2-1 Minamijosanjia, Tokushima city, Tokushima 770-8506, Japan
Professor, Institute of Engineering, Hiroshima University
1-4-1 Kagamiyama, Higashi-hiroshima city, Hiroshima 739-8527, Japan
Associate Professor, Department of Intelligent Mechanical Systems, Kumamoto University
2-39-1 Kurokami, Chuo-ku, Kumamoto 860-8555, Japan
Associate Professor, Department of Electro-Mechanical Engineering, National Institute of Technology, Kagawa College
355 Chokushicho, Takamatsu, Kagawa 761-8058, Japan
When first introduced half a century ago, adaptive control was half accepted as useful and half rejected as useless in industrial systems, and has greatly evolved theoretically. Learning control, a related discipline, has also been widely studied, especially in robot control. Adaptive/learning control, which incorporates the two, has become trendy in Japan and elsewhere. New design methods, e.g., data-driven controllers and the machine learning based controllers, are also attracting attention.
This special issue, which focuses on adaptive/learning control, includes 18 contributions classified as follows:
- • Closed-loop identification and controller redesign
- • Adaptive output feedback control
- • Data-driven control
- • Multirate control
- • Computational intelligence-based approaches
- • Applications centering on electric motors, engine systems, hydraulic excavators, rotary cranes, etc.
In addition, one review paper covers performance-driven control.
The theoretical study of adaptive/learning control has few actual applied examples in the form of real systems but is flourishing. Applied studies are expected to increasingly progress and adaptive/learning control theory holds big changes for industrial fields.