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Special Issue "Machine Learning in Cyber Physical Systems"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Industrial Sensors".

Deadline for manuscript submissions: 30 June 2023 | Viewed by 274

Special Issue Editors

Department of Engineering, School of Science and Technology, Clifton Campus, Nottingham Trent University, Nottingham NG11 8NS, UK
Interests: robotics; cyber physical production systems; digital twins; human–robot collaboration; precision engineering
Department of Computer Science and Technology, Faculty of Science and Engineering, University of Hull, Hull HU6 7RX, UK
Interests: mechatronics; robotics; control systems
Special Issues, Collections and Topics in MDPI journals
Department of Engineering, School of Science and Technology, Clifton Campus, Nottingham Trent University, Nottingham NG11 8NS, UK
Interests: Industry 4.0; machine learning; cyber physical systems; blockchain
TU Braunschweig, Braunschweig, Germany
Interests: model predictive control; robotics; reinforcement learning; digitalization

Special Issue Information

Dear Colleagues,

Industrial systems worldwide are undergoing a paradigm shift towards Industry 4.0. Self-decision making is the core characteristic of such systems, where machine intelligence is employed to accomplish tasks. Cyber physical systems is one such area of enabling technology necessary to create a seamless integration of cyber and physical components. The digital twinning of physical systems is rapidly developing, and can correlate large real-time sensing and IoT data. Sensor fusion, machine learning, AI, and other advanced techniques are applied to create a dynamic virtual representation of entire systems.

Consequently, this Special Issue seeks innovative works on a wide range of research topics spanning Industry 4.0-related technologies, including but not restricted to the following topics:

  1. All aspects of cyber physical production systems including sensing, robotics, machine learning, big data analytics and system vulnerability.
  2. CPS applications in logistics and vehicular networks, supply chain and blockchain implementation. 
  3. Digital twin development, AR/VR and seamless integration with physical systems.
  4. Machine learning, deep learning and reinforcement learning use cases.
  5. Use of neural networks in modelling complex systems.
  6. Advanced control schemes including state space and model predictive control. 

Dr. Azfar Khalid
Dr. Jamshed Iqbal
Dr. Reza Vatankhah Barenji
Prof. Dr. Jürgen Pannek
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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. Sensors 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.


  • cyber physical production systems
  • machine learning
  • deep learning
  • reinforcement learning
  • digital twins
  • control systems
  • predictive control

Published Papers

This special issue is now open for submission.
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