Special Issue on New Development in Adaptive & Learning Control

Shoichiro Fujisawa; Toru Yamamoto; Ikuro Mizumoto; Tomohiro Henmi

doi:10.20965/jrm.2016.p0615

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JRM Vol.28 No.5 p. 615

doi: 10.20965/jrm.2016.p0615

(2016)

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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

Published:

October 20, 2016

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.

Cite this article as:

S. Fujisawa, T. Yamamoto, I. Mizumoto, and T. Henmi, “Special Issue on New Development in Adaptive & Learning Control,” J. Robot. Mechatron., Vol.28 No.5, p. 615, 2016.

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