Control Applications and Learning

Dear Colleagues,

The proper design of controllers for various kinds of systems involving unknown conditions and highly nonlinear and uncertain dynamics remains an open research topic. Meanwhile, machine-learning-based algorithms have been used in several fields, especially when massive amounts of data and great computing power are needed. Research in the field of machine learning aiming to solve issues of flexibility and complexity is ongoing. The connection between (modern) control theory and machine learning is very important in view of surpassing the potentialities of each discipline.

On this note, "control and learning" techniques are presently used in the driving technology underpinning a whole new generation of autonomous devices and cognitive artifacts that, through their learning capabilities, interact seamlessly with the world around them, hence providing the missing link between the digital and physical worlds.

Moreover, control and learning techniques are often used in various industries for control, fault detection, fault diagnosis, and fault-tolerant control. To address these issues, there is a need to develop hybrid algorithms based on control and/or learning; such algorithms can be recommended in this Special Issue.

This Special Issue will focus on control, modeling, various machine learning techniques, fault diagnosis, and fault-tolerant control for systems. Papers specifically addressing the theoretical, experimental, practical, and technological aspects of modeling, control, fault diagnosis, and fault-tolerant control of various systems and extending concepts and methodologies from classical techniques to hybrid methods will be highly suitable for this Special Issue. Potential themes include, but are not limited to:

• Modeling and identification
• Adaptive and hybrid control
• Adaptive and hybrid observers
• Reinforcement learning for control
• Data-driven control
• Fault diagnosis
• Fault-tolerant control of systems based on various control and learning techniques

Prof. Dr. Jong-Myon Kim
Prof. Dr. Hyeung-Sik Choi
Dr. Farzin Piltan
Guest Editors

Manuscript Submission Information

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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.

• robust/nonlinear control algorithms
• machine learning algorithms
• system modeling and identification techniques
• fault diagnosis/prognosis and fault-tolerant control using hybrid techniques
• adaptive and hybrid control techniques
• adaptive and hybrid observation techniques
• reinforcement learning for control
• data-driven control