Sensing and Machine Learning Control: Progress and Applications
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 20 March 2025 | Viewed by 94
Special Issue Editor
Interests: machine learning; reinforcement learning; adaptive control; recommender systems; user modeling;wastewater systems; adaptive predictive control; adaptive interfaces
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The Special Issue "Sensing and Machine Learning Control: Progress and Applications" delves into the dynamic integration of sensing technology and machine learning (ML) with control systems, highlighting cutting-edge research and innovative applications. It brings together a collection of studies that demonstrate how ML techniques, enhanced by advanced sensing technologies, can significantly improve control processes across various domains.
The issue covers diverse topics, including reinforcement learning for develo** adaptive control strategies, the application of deep learning for predictive maintenance and fault detection, and the design of hybrid systems combining traditional control theories with advanced ML algorithms. These contributions illustrate how ML, coupled with precise and real-time sensor data, can address complex, nonlinear dynamics and improve the efficiency, accuracy, and robustness of control systems.
Practical applications featured in the issue span multiple industries, such as autonomous vehicles, robotics, energy management, and aerospace. Case studies showcase the practical benefits of sensor-enhanced, ML-driven control, such as improved performance, cost savings, and increased safety.
This Special Issue serves as a crucial resource for researchers, engineers, and practitioners, highlighting ongoing advancements and encouraging further exploration into the synergy between sensing technology, machine learning, and control systems. It underscores the significant potential of integrating sensors with ML to revolutionize traditional control methodologies and drive future innovations.
Dr. Félix Hernández del Olmo
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- sensing technology
- autonomous vehicles
- robotics
- energy management
- ML-driven control
