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Applied Sciences
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Control and Security of Industrial Cyber–Physical Systems
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Control and Security of Industrial Cyber–Physical Systems

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 August 2026 | Viewed by 4021

Special Issue Editors

Prof. Dr. Meng Zhang

E-Mail Website
Guest Editor
School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: nonlinear control; filtering; power systems; cyber-physical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Meng Li

E-Mail Website
Guest Editor
School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: networked control system; cyber-physical systems; robust control; optimal control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industrial cyber–physical system is a complex system that deeply integrates information technologies such as computing, communication, and control with physical systems in industrial production. Its core lies in achieving intelligence, precision, and efficiency in industrial production through bidirectional interaction and collaboration between information and physics. The typical application of industrial cyber-physical systems including smart grid, robots, autonomous unmanned systems, intelligent transportation, and more.

Due to the deep coupling between physical systems and cyber systems, the monitoring, modeling, control, and security of cyber–physical systems face unprecedented technological challenges. This Special Issue aims to showcase the latest advancements in the control and security field of industrial cyber–physical systems. Authors are invited to contribute original research papers and conceptual articles addressing various aspects of cyber–physical systems.

Suitable topics include, but are not limited to, the following:

  • Distributed Optimization and Control;
  • Data Driven Learning and Control;
  • Collaborative Control of Multi-agent Systems;
  • Secure Control and Optimization;
  • Attack and fault detection;
  • Privacy Protection and Differential Privacy;
  • The applications in smart grid, robots, etc.

Prof. Dr. Meng Zhang
Dr. Meng Li
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 250 words) can be sent to the Editorial Office for assessment.

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.

Keywords

  • cyber-physical systems
  • fault detection
  • collaborative control of multi-agent systems

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Published Papers (2 papers)

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Research

25 pages, 2140 KB  
Article
A Bearing Fault Diagnosis Method for Multi-Sensors Using Cloud Model and Dempster–Shafer Evidence Fusion
by Lin Li, Xiafei Zhang, Peng Wang, Chaobo Chen, Tianli Ma and Song Gao
Appl. Sci. 2025, 15(21), 11302; https://doi.org/10.3390/app152111302 - 22 Oct 2025
Viewed by 1300
Abstract
This paper proposes a bearing fault diagnosis method based on the Dempster–Shafer evidence fusion of cloud model memberships from multi-channel data, which provides an explicable calculation process and a final result. Firstly, vibration signals from the drive end and fan end of the [...] Read more.
This paper proposes a bearing fault diagnosis method based on the Dempster–Shafer evidence fusion of cloud model memberships from multi-channel data, which provides an explicable calculation process and a final result. Firstly, vibration signals from the drive end and fan end of the rolling bearing are used as dual-channel data sources to extract multi-dimensional features from time and frequency domains. Then, cloud models are employed to build models for each feature under different conditions, utilizing three digital characteristic parameters to characterize the distribution and uncertainty of features under different operating conditions. Thus, the membership degree vectors of test samples from two channels can be calculated using reference models. Subsequently, D-S evidence theory is applied to fuse membership degree vectors of the two channels, effectively enhancing the robustness and accuracy of the diagnosis. Experiments are conducted on the rolling bearing fault dataset from Case Western Reserve University. Results demonstrate that the proposed method achieves an accuracy of 96.32% using evidence fusion of the drive-end and fan-end data, which is obviously higher than that seen in preliminary single-channel diagnosis. Meanwhile, the final results can give suggestions of the possibilities of anther, which is benefit for technicists seeking to investigate the actual situation. Full article
(This article belongs to the Special Issue Control and Security of Industrial Cyber–Physical Systems)
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18 pages, 780 KB  
Article
Multi-Source Energy Storage Day-Ahead and Intra-Day Scheduling Based on Deep Reinforcement Learning with Attention Mechanism
by Enren Liu, Song Gao, Xiaodi Chen, Jun Li, Yuntao Sun and Meng Zhang
Appl. Sci. 2025, 15(18), 10031; https://doi.org/10.3390/app151810031 - 14 Sep 2025
Cited by 2 | Viewed by 2060
Abstract
With the rapid integration of high-penetration renewable energy, its inherent uncertainty complicates power system day-ahead/intra-day scheduling, leading to challenges like wind curtailment and high operational costs. Existing methods either rely on inflexible physical models or use deep reinforcement learning (DRL) without prioritizing critical [...] Read more.
With the rapid integration of high-penetration renewable energy, its inherent uncertainty complicates power system day-ahead/intra-day scheduling, leading to challenges like wind curtailment and high operational costs. Existing methods either rely on inflexible physical models or use deep reinforcement learning (DRL) without prioritizing critical variables or synergizing multi-source energy storage and demand response (DR). This study develops a multi-time scale coordination scheduling framework to balance cost minimization and renewable energy utilization, with strong adaptability to real-time uncertainties. The framework integrates a day-ahead optimization model and an intra-day rolling model powered by an attention-enhanced DRL Actor–Critic network—where the attention mechanism dynamically focuses on critical variables to correct real-time deviations. Validated on an East China regional grid, the framework significantly enhances renewable energy absorption and system flexibility, providing a robust technical solution for the economical and stable operation of high-renewable power systems. Full article
(This article belongs to the Special Issue Control and Security of Industrial Cyber–Physical Systems)
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