Data-Driven and Machine Learning Approaches in Intelligent Control

Deadline for manuscript submissions: June 12, 2026

 

Special Issue Editors

Dr. Guoqiang Tan  Website  E-Mail: g.tan@lboro.ac.uk
Guest Editor
Loughborough University, United Kingdom            
Interests: optimal control; neural networks; state estimation; stability analysis; autonomous systems; power systems; reinforcement learning

 

Special Issue Information

This Special Issue aims to address intelligent control challenges through data-driven and machine learning approaches.

As modern industrial processes become increasingly complex, there is a growing demand for intelligent control systems that leverage big data and real-time learning in dynamic environments.

This Special Issue explores the integration of data-driven technologies and machine learning to solve intelligent control problems, covering topics such as intelligent control, machine learning, data-driven approaches, and industrial automation, with a focus on innovative methodologies and real-world applications. By enabling adaptive, efficient, and autonomous decision-making in complex environments, it paves the way for the establishment of a high-level academic exchange platform and propels the field of intelligent control to new heights.

 

Keywords

intelligent control; machine learning; data-driven; industrial automation

 

 

Published Papers