Special Sections

A current special section is devoted to the intersection of Machine Learning with Control. Unprecedented technological advances have fueled the creation of devices that can collect, generate, store, and transfer large amounts of data. This massive data outpour is profoundly changing the way in which complex engineering problems are solved, calling for the conception of new interdisciplinary tools at the intersection of machine learning, dynamic systems and control, and optimization. While the repurposing of control theories building on new Machine Learning methods can be highly successful, Dynamic Systems and Control can greatly contribute to analyze and devise novel adaptive, safety-critical controllers with performance guarantees. This special issue aims to contribute to this growing area of interest and calls thus for papers in this topical area. 

Keywords:

• Machine learning for dimensionality reduction and system identification
• Emerging applications for learning-based control
• Data-driven optimization and control for dynamical systems
• Safe reinforcement learning and safe adaptive control
• Bridging model-based and learning-based control systems
• Distributed learning over distributed systems
• Reinforcement learning for multiagent systems
• Optimization, dynamics and control for machine learning
• Reinforcement learning and statistical learning for dynamical and control systems