Advances in Machine Learning, Optimization and Control Applications, 2nd Edition

Dear Colleagues,

In practice, many systems such as industrial processes, aerospace systems, transportation systems and power grid systems are becoming increasingly complex. Moreover, these systems may suffer from various uncertainties, high nonlinearities, external disturbances, stochastic effects, etc., which significantly challenge model-based control and optimization, while with the development of information science and sensing technology, huge amounts of data are constantly emerging. Both academia and industry have put much effort into mining valuable information from data to facilitate the control and optimization of practical systems.

Over the past few decades, data science and machine learning have demonstrated tremendous success in many areas of science and engineering, such as large-scale pattern recognition, computer vision, multiagent control and industrial engineering. The connection between machine learning and control theory is becoming a popular research topic, which may endow control systems with learning ability and thus improve the control ability and performance of conventional control approaches. However, the coupling of a learning algorithm with a control loop requires a combined treatment as a dynamic process, which raises fundamental questions about stability, robustness and safety for control systems. Additionally, insights from robust control theory may, in turn, help to enhance the robustness of machine learning algorithms. In order to leverage the potential of data-based learning methods for control and optimization, we therefore believe that principled approaches integrating with machine learning and control theory are urgently needed, creating new demands for novel mathematical theory, new optimization algorithms and statistical techniques.

This Special Issue on “Advances in Machine Learning, Optimization and Control Applications II” aims to present the latest theoretical and technical advancements in the broad areas of machine learning, optimization and control applications, and also to explore potential problems and challenges in connection to these techniques. Topics of interest in this Special Issue include but are not limited to machine learning, neural networks, statistical optimization learning, parallel and distributed optimization, sparse optimization, intelligent control via neural networks, and other applications of machine learning.

Prof. Dr. Wanquan Liu
Dr. Xuefang Li
Dr. Xianchao Xiu
Guest Editors

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• machine learning
• neural networks
• mathematical models
• distributed systems
• optimization methods
• scientific computing
• pattern recognition
• data-driven control systems
• learning control systems
• reinforcement-learning control and optimization

• Advances in Machine Learning, Optimization, and Control Applications in Mathematics (11 articles)