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Electronics
Special Issues
Recent Progresses and Applications in Automatic Intelligent Control
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Recent Progresses and Applications in Automatic Intelligent Control

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 2777

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
Prof. Dr. Yulong Wang

E-Mail Website
Guest Editor
School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China
Interests: intelligent unmanned systems; situational awareness in marine environment; ship motion control
Prof. Dr. Qingkui Li

E-Mail Website
Guest Editor
School of Automation, Beijing Information and Technology University, Beijing 100192, China
Interests: switched systems; networked control systems; supply chain systems

Special Issue Information

Dear Colleagues,

As an important field in the computer simulation of human intelligence, automatic intelligent control is a type of automatic control that can autonomously drive intelligent systems to achieve their goals without human intervention. Automatic intelligent control can cope with control problems in complicated systems characterized by non-determined mathematical models, high degree of nonlinearity and complex task requirements based on artificial-intelligence-driven learning, reasoning and decision making. In recent decades, automatic intelligent control has been widely applied in industrial and socioeconomic systems, such as advanced manufacturing systems, supply chain systems, logistical transportation systems, electrical power systems, etc.

The primary objective of this Special Issue is to focus on the up-to-date methodologies and applications of automatic intelligent control. Research papers that employ theoretical analysis and/or practical applications in the related scope are welcomed. The topics of interest include but are not limited to:

  • Artificial intelligence based automatic intelligent control;
  • Reinforcement learning in automatic intelligent control;
  • Data-driven automatic intelligent control;
  • Learning human by demonstrations in automatic intelligent control;
  • Application of automatic intelligent control in real-world control systems.

Prof. Dr. Hongfeng Wang
Prof. Dr. Yulong Wang
Prof. Dr. Qingkui 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 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 control
  • human intelligence
  • data driven
  • adaptation and learning control

Published Papers (2 papers)

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Research

20 pages, 2701 KiB  
Article
A Product-Design-Change-Based Recovery Control Algorithm for Supply Chain Disruption Problem
by Jingze Chen, Haodong Kang and Hongfeng Wang
Electronics 2023, 12(12), 2552; https://doi.org/10.3390/electronics12122552 - 06 Jun 2023
Cited by 2 | Viewed by 1003
Abstract
In very recent years, large-scale disruptions brought by major global and local emergencies have posed many challenges with respect to the recovery control of supply chain systems. This work investigates a problem regarding the optimal control of a supply chain by considering product [...] Read more.
In very recent years, large-scale disruptions brought by major global and local emergencies have posed many challenges with respect to the recovery control of supply chain systems. This work investigates a problem regarding the optimal control of a supply chain by considering product design change in order to enable manufacturers to recover their disrupted supply chain quickly. A two-layer optimization model is developed, in which the lower model is used to optimize the product design change path, and the upper model is used to select the appropriate alternative suppliers and schedule the delivery of customer orders. To solve the developed model, a hybrid ant colony optimization (HACO) algorithm is designed, which is combined with a Gurobi solver and uses some special strategies. The validity of the proposed algorithm is illustrated experimentally through computational tests and systematic comparison with the existing methods. It is reported that the losses caused by supply chain disruptions are reduced significantly. The proposed model and algorithm can provide a potentially useful tool that can help manufacturers decide upon the optimal form of recovery control when a supply chain system experiences a massive supply disruption. Full article
(This article belongs to the Special Issue Recent Progresses and Applications in Automatic Intelligent Control)
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23 pages, 2400 KiB  
Article
Decomposition-Based Bayesian Network Structure Learning Algorithm for Abnormity Diagnosis Model for Coal Mill Process
by Yuqing Chang, Leyuan Liu, Xiaoyun Kang and Fuli Wang
Electronics 2022, 11(23), 3870; https://doi.org/10.3390/electronics11233870 - 23 Nov 2022
Cited by 4 | Viewed by 1133
Abstract
In the structure learning of the large-scale Bayesian network (BN) model for the coal mill process, taking the view of the problem that the decomposition-based method cannot guarantee the sufficient learning of abnormal state node neighborhood in the diagnosis model, this paper proposes [...] Read more.
In the structure learning of the large-scale Bayesian network (BN) model for the coal mill process, taking the view of the problem that the decomposition-based method cannot guarantee the sufficient learning of abnormal state node neighborhood in the diagnosis model, this paper proposes a new BN structure learning method based on decomposition. Firstly, a sketch is constructed based on an improved Markov blanket discovery algorithm and edge thickening and thinning. Second, the node centrality of k-path is used to search the important nodes, and the subgraph decomposition is realized by extracting these important nodes and their neighborhoods from the sketch. Then, through the targeted design of subgraph de-duplication, subgraph learning, and subgraph reorganization methods, the learning of large-scale BN is realized. This method is applied to public data sets, and its advantages and disadvantages are analyzed by comparing them with other methods. The advantage of the BN structure learning method of the abnormal condition diagnosis model is further verified by applying the method to the coal mill process, which is consistent with the original design intention. Full article
(This article belongs to the Special Issue Recent Progresses and Applications in Automatic Intelligent Control)
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