Disruptive Trends in Automation Technology

Dear Colleagues,

The industrial sector is being transformed by the convergence of information technology and operational technology. The latter is another name for automation technology and covers established systems such as supervisory control and data acquisition (SCADA), programmable logic controllers (PLC), fieldbuses and automation and control systems. As this technology is connected to the Internet and 5G networks, some monitoring, control and analytic functionalities are deployed to the edge or cloud, and researchers are challenged to ensure the security, dependability, real-time performance and maintainability of the resulting systems. The big data that is accessible from these systems create opportunities for artificial intelligence applications that can further disrupt the established practices in the automation domain. For example, reinforcement learning is emerging as an alternative technology for industrial process control and optimization, and machine learning is heavily applied to fault diagnostic and predictive maintenance. Real-time connectivity, cloudification, big data and artificial intelligence are all driving the transformation of conventional simulators to digital twins.

In this Special Issue, we welcome contributions on advances in automation technology, especially but not limited to the abovementioned disruptive developments. Survey papers and reviews are also welcomed.

Dr. Seppo Sierla
Dr. David Hästbacka
Dr. Kai Zenger
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 2300 CHF (Swiss Francs). For papers submitted to this special issue through the collaboration with the Automation Days 2023 conference (https://www.automaatioseura.fi/automationdays2023/), the APC is 1955 CHF. 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.