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Electronics
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Advanced Intelligent Control and Automation in Industrial 4.0 Era
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Advanced Intelligent Control and Automation in Industrial 4.0 Era

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: 15 January 2025 | Viewed by 2175

Special Issue Editors

Prof. Dr. Hongfeng Wang

E-Mail Website
Guest Editor
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: intelligent manufacturing, logistics and supply chain management; evolutionary computation; reinforcement learning
Special Issues, Collections and Topics in MDPI journals
Dr. Bo Zhou

E-Mail Website
Guest Editor
Shenyang Institute of Automation (SIA), Chinese Academy of Sciences, Shenyang 110169, China
Interests: Intelligent manufacturing; Additive manufacturing; Robot manufacturing; Path planning; Intelligent assembly

Special Issue Information

Dear Colleagues,

“Human-centricity” has emerged as a defining feature of the next stage of Industry 4.0, which means that intelligent manufacturing has entered a new transformation stage: from focusing on reducing the physical labor of operators to reducing their mental labor, from prioritizing shareholders to stakeholders, and the rise in value-driven manufacturing relative to technology-driven manufacturing. Two technological advancements play a crucial role in advanced manufacturing: intelligent control and automation. The ways to achieve the goals of Industry 4.0 include creating and implementing advanced automation technologies that are related to the design of control systems in a broader sense. In addition, intelligent control will further elevate automation technology, replacing operators with artificial intelligence for decision-making.

Advanced intelligent control and automation have demonstrated their enormous potential through successful implementation in various fields of the industry. They can be argued to have a considerable impact on the progress of the global promotion of Industry 4.0. Therefore, this Special Issue places importance on advanced intelligent control and automation. We would like to invite submissions of high-quality manuscripts from researchers, engineers, and industry professionals for publication in this Special Issue. The manuscripts should be unpublished and report significant research progress. The key criteria for paper acceptance will be novelty. Manuscripts reporting experimental proofs, results, and lessons learned are strongly encouraged. Review papers on the state of the art of different topics related to advanced intelligent control and automation are also welcome. The main topics of interest include, but are not limited to, the following:

  • Automation control systems;
  • Manufacturing execution system;
  • Advanced manufacturing technologies;
  • Digital twins and the industrial metaverse;
  • Enterprise resource planning;
  • Artificial intelligence;
  • Intelligent industrial robotics;
  • Robot and computer-integrated manufacturing.

Prof. Dr. Hongfeng Wang
Dr. Bo Zhou
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 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. Electronics 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

  • intelligent manufacturing
  • advanced intelligent control
  • automation
  • industry 4.0
  • robotics
  • manufacturing execution system

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Published Papers (2 papers)

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22 pages, 9220 KiB  
Article
Anti-Sway Adaptive Fast Terminal Sliding Mode Control Based on the Finite-Time State Observer for the Overhead Crane System
by Xin Wang, Zhenxin He, Chuntong Liu and Wenzheng Du
Electronics 2024, 13(23), 4709; https://doi.org/10.3390/electronics13234709 - 28 Nov 2024
Viewed by 352
Abstract
This work proposes an adaptive rapid terminal SMC (sliding mode control) approach based on the FFTSO (fast finite-time state observer) for overhead crane trajectory tracking and anti-swing control in the presence of external disturbances and parameter uncertainty. First, the system state observation under [...] Read more.
This work proposes an adaptive rapid terminal SMC (sliding mode control) approach based on the FFTSO (fast finite-time state observer) for overhead crane trajectory tracking and anti-swing control in the presence of external disturbances and parameter uncertainty. First, the system state observation under the constraint of unknown system parameters is accomplished by designing the FFTSO based on finite-time theory. Next, a parameter-adaptive fast terminal SMC is created for an overhead crane based on the model transformation. This technique can still monitor the intended trajectory and reduce payload swing even in cases when the payload mass and wire rope length are uncertain. Next, the Lyapunov theorem is used to demonstrate the stability of the overhead crane system’s positioning and anti-swing angle control mechanism. Lastly, the platform experiments confirm that the suggested closed-loop system control technique is successful. Full article
(This article belongs to the Special Issue Advanced Intelligent Control and Automation in Industrial 4.0 Era)
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27 pages, 5710 KiB  
Article
A Deep Reinforcement Learning Method Based on a Transformer Model for the Flexible Job Shop Scheduling Problem
by Shuai Xu, Yanwu Li and Qiuyang Li
Electronics 2024, 13(18), 3696; https://doi.org/10.3390/electronics13183696 - 18 Sep 2024
Viewed by 1296
Abstract
The flexible job shop scheduling problem (FJSSP), which can significantly enhance production efficiency, is a mathematical optimization problem widely applied in modern manufacturing industries. However, due to its NP-hard nature, finding an optimal solution for all scenarios within a reasonable time frame faces [...] Read more.
The flexible job shop scheduling problem (FJSSP), which can significantly enhance production efficiency, is a mathematical optimization problem widely applied in modern manufacturing industries. However, due to its NP-hard nature, finding an optimal solution for all scenarios within a reasonable time frame faces serious challenges. This paper proposes a solution that transforms the FJSSP into a Markov Decision Process (MDP) and employs deep reinforcement learning (DRL) techniques for resolution. First, we represent the state features of the scheduling environment using seven feature vectors and utilize a transformer encoder as a feature extraction module to effectively capture the relationships between state features and enhance representation capability. Second, based on the features of the jobs and machines, we design 16 composite dispatching rules from multiple dimensions, including the job completion rate, processing time, waiting time, and manufacturing resource utilization, to achieve flexible and efficient scheduling decisions. Furthermore, we project an intuitive and dense reward function with the objective of minimizing the total idle time of machines. Finally, to verify the performance and feasibility of the algorithm, we evaluate the proposed policy model on the Brandimarte, Hurink, and Dauzere datasets. Our experimental results demonstrate that the proposed framework consistently outperforms traditional dispatching rules, surpasses metaheuristic methods on larger-scale instances, and exceeds the performance of existing DRL-based scheduling methods across most datasets. Full article
(This article belongs to the Special Issue Advanced Intelligent Control and Automation in Industrial 4.0 Era)
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