Special Issue on New Development in Adaptive & Learning Control

Abstract

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.