Data-Driven Control System: Methods and Applications

Dear Colleagues,

The advent of data-driven strategies in control systems engineering is profoundly influencing technological advancements across numerous domains. This Special Issue, titled "Data-Driven Control Systems: Methods and Applications", seeks to present innovative ideas and experimental outcomes in the realm of data-driven control from theoretical concepts to practical implementations.

This Special Issue aims to cover a wide array of topics within data-driven control systems, including, but not limited to, the following: development and application of machine learning algorithms for real-time control, adaptive control mechanisms that learn from data in situ, and the utilization of big data analytics for enhancing control strategies. Additionally, we are interested in papers that explore the integration of IoT technologies with control systems to push the boundaries of automation and efficiency, as well as research that addresses the challenges of security, privacy, and robustness in these systems.

Specific areas of focus include advanced algorithmic solutions that facilitate predictive and adaptive control, techniques for managing and analyzing massive datasets in real-time to improve system responses, and the design of resilient architectures that support the demands of data-intensive, high-performance control applications. Theoretical explorations that ensure stability and reliability in control systems, along with practical applications demonstrating significant enhancements in sectors such as manufacturing, robotics, and smart grids, are particularly welcome.

We invite submissions of high-quality, original research papers that contribute to the following fields:

• Data-driven algorithms for dynamic control systems;
• Real-time analytics and decision-making processes;
• Adaptive control techniques using machine learning;
• Security and data privacy in networked control systems;
• Sustainable and energy-efficient control solutions;
• Data-driven controls in industry.

Dr. Wenfeng Hu
Dr. M. (Meichen) Guo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• data-driven control
• machine learning in control systems
• networked control systems
• adaptive control systems
• real-time systems analytics
• big data in control applications
• sustainable and energy-efficient controls
• security and privacy in data-driven systems