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Mathematics
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Complex Process Modeling and Control Based on AI Technology
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Complex Process Modeling and Control Based on AI Technology

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 1 April 2025 | Viewed by 1832

Special Issue Editors

Prof. Dr. Jie Hu

E-Mail Website
Guest Editor
School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: process control; intelligent control; computational intelligence
Prof. Dr. Sheng Du

E-Mail Website
Guest Editor
School of Automation, China University of Geosciences, Wuhan 430074, China
Interests: process control; intelligent control; intelligent optimization; computational intelligence; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Pan Yu

E-Mail Website
Guest Editor
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Interests: multi-agent cooperative control; high-precision control of electromechanical systems; anti-disturbance control; modern robust control; control theory and applications; repetitive control

Special Issue Information

Dear Colleagues,

In recent years, with the rapid development of big data and artificial intelligence technology, how to apply these new technologies to complex systems has attracted the attention of many scholars. The aim of this Special Issue is to explore complex process modeling, optimization and control, as well as the important support for the application of advanced methods and techniques of machine learning and artificial intelligence to complex processes.

This Special Issue will focus on complex process modeling and control based on AI technology and will present considerable novelty in both theoretical background and practical design. Papers should provide original ideas and new approaches, and should clearly indicate progress made in problem formulation, methodology, or application. Research areas may include (but are not limited to) the following:

  • Hybrid intelligent modeling techniques;
  • Data-driven modelling techniques;
  • Modeling and optimization of complex industrial processes;
  • Online measurement, process control, and optimization for cyber–physical systems;
  • Data mining and management methods for massive volumes of data;
  • Machine learning applications to manufacturing automation.

Prof. Dr. Jie Hu
Prof. Dr. Sheng Du
Dr. Pan Yu
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. Mathematics 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 2600 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

  • data-driven modeling
  • artificial intelligence
  • process control
  • dynamical systems modelling
  • machine learning

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Published Papers (3 papers)

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Research

12 pages, 483 KiB  
Article
Anti-Disturbance Bumpless Transfer Control for a Switched Systems via a Switched Equivalent-Input-Disturbance Approach
by Jiawen Wu, Qian Liu and Pan Yu
Mathematics 2024, 12(15), 2307; https://doi.org/10.3390/math12152307 - 23 Jul 2024
Viewed by 299
Abstract
This paper concentrates on the issue of anti-disturbance bumpless transfer (ADBT) control design for switched systems. The ADBT control design problem refers to designing a continuous controller and a switching rule to ensure the switched system satisfies the ADBT property. First, the concept [...] Read more.
This paper concentrates on the issue of anti-disturbance bumpless transfer (ADBT) control design for switched systems. The ADBT control design problem refers to designing a continuous controller and a switching rule to ensure the switched system satisfies the ADBT property. First, the concept of the ADBT property is introduced. Then, via a switched equivalent-input-disturbance (EID) methodology, a switched EID estimator is formulated to estimate the impact of external disturbances within the switched system. Second, a bumpless transfer control is then constructed via a compensator integrating an EID estimation. Finally, the effectiveness of the presented control scheme is verified by controlling a switching resistor–inductor–capacitor circuit on the Matlab platform. Above all, a new configuration for ADBT control of switched systems is established via a switched EID methodology. Full article
(This article belongs to the Special Issue Complex Process Modeling and Control Based on AI Technology)
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12 pages, 955 KiB  
Article
Time-Series Prediction of Electricity Load for Charging Piles in a Region of China Based on Broad Learning System
by Liansong Yu and Xiaohu Ge
Mathematics 2024, 12(13), 2147; https://doi.org/10.3390/math12132147 - 8 Jul 2024
Viewed by 637
Abstract
This paper introduces a novel electricity load time-series prediction model, utilizing a broad learning system to tackle the challenge of low prediction accuracy caused by the unpredictable nature of electricity load sequences in a specific region of China. First, a correlation analysis with [...] Read more.
This paper introduces a novel electricity load time-series prediction model, utilizing a broad learning system to tackle the challenge of low prediction accuracy caused by the unpredictable nature of electricity load sequences in a specific region of China. First, a correlation analysis with mutual information is utilized to identify the key factors affecting the electricity load. Second, variational mode decomposition is employed to obtain different mode information, and then a broad learning system is utilized to build a prediction model with different mode information. Finally, particle swarm optimization is used to fuse the prediction models under different modes. Simulation experiments using real data validate the efficiency of the proposed method, demonstrating that it offers higher accuracy compared to advanced modeling techniques and can assist in optimal electricity-load scheduling decision-making. Additionally, the R2 of the proposed model is 0.9831, the PRMSE is 21.8502, the PMAE is 17.0097, and the PMAPE is 2.6468. Full article
(This article belongs to the Special Issue Complex Process Modeling and Control Based on AI Technology)
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13 pages, 1279 KiB  
Article
Fault Distance Measurement in Distribution Networks Based on Markov Transition Field and Darknet-19
by Haozhi Wang, Wei Guo and Yuntao Shi
Mathematics 2024, 12(11), 1665; https://doi.org/10.3390/math12111665 - 27 May 2024
Cited by 1 | Viewed by 515
Abstract
The modern distribution network system is gradually becoming more complex and diverse, and traditional fault location methods have difficulty in quickly and accurately locating the fault location after a single-phase ground fault occurs. Therefore, this study proposes a new solution based on the [...] Read more.
The modern distribution network system is gradually becoming more complex and diverse, and traditional fault location methods have difficulty in quickly and accurately locating the fault location after a single-phase ground fault occurs. Therefore, this study proposes a new solution based on the Markov transfer field and deep learning to predict the fault location, which can accurately predict the location of a single-phase ground fault in the distribution network. First, a new phase-mode transformation matrix is used to take the fault current of the distribution network as the modulus 1 component, avoiding complex calculations in the complex field; then, the extracted modulus 1 component of the current is transformed into a Markov transfer field and converted into an image using pseudo-color coding, thereby fully exploiting the fault signal characteristics; finally, the Darknet-19 network is used to automatically extract fault features and predict the distance of the fault occurrence. Through simulations on existing models and training and testing with a large amount of data, the experimental results show that this method has good stability, high accuracy, and strong anti-interference ability. This solution can effectively predict the distance of ground faults in distribution networks. Full article
(This article belongs to the Special Issue Complex Process Modeling and Control Based on AI Technology)
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