Latest Application of Artificial Intelligence in Industrial Process Modelling and Optimization

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "AI-Enabled Process Engineering".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 386

Special Issue Editors


E-Mail Website
Guest Editor
School of Artificial Intelligence and 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

E-Mail Website
Guest Editor
School of Information Science and Technology, Beijing University of Technology, Beijing 100124, China
Interests: robot control; multi-agent cooperative control; high-precision control of electromechanical systems; active disturbance rejection control; advanced robust control; control theory and application
Special Issues, Collections and Topics in MDPI journals
School of Automation, Hubei University of Science and Technology, Xianning 437100, China
Interests: theory of active defense for information-physical systems; privacy-preserving system state estimation and control; robot intelligent control; telematics security and control
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As a revolutionary technology, artificial intelligence (AI) has shown great potential in industrial process modelling and optimization, and the application of explainable artificial intelligence (XAI) further strengthens its practicability in industrial scenarios. It can reduce the reliance on prior knowledge via autonomous process learning, improve modelling accuracy, optimize control strategies for better stability and performance, adapt to dynamic complex environments, and achieve intelligent monitoring and diagnosis through data analysis to ensure production safety and efficiency. This Special Issue focuses on the in-depth exploration of AI and XAI applications in this field, aiming to promote innovative research and realize the synergistic improvement of efficiency, safety and sustainability in various industrial processes.

Prof. Dr. Sheng Du
Prof. Dr. Li Jin
Dr. Pan Yu
Dr. Hao Liu
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. Processes 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

  • industrial process modeling
  • intelligent optimization
  • artificial intelligence
  • decision support systems
  • cyber–physical systems

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Other

55 pages, 1655 KB  
Systematic Review
AI in Robot Manipulator Control: A Systematic Review
by Charles C. Nguyen, Ha T. T. Ngo, Tu T. C. Duong, Tri T. Nguyen, Tuan M. Nguyen and Lu Sun
Processes 2026, 14(9), 1401; https://doi.org/10.3390/pr14091401 - 27 Apr 2026
Viewed by 224
Abstract
This study presents a PRISMA-based systematic review of 343 publications focused on tracking how AI-based methods have evolved within robot manipulator control from 2015 to 2025. The review examines how AI has been incorporated into the control pipeline by organizing prior work according [...] Read more.
This study presents a PRISMA-based systematic review of 343 publications focused on tracking how AI-based methods have evolved within robot manipulator control from 2015 to 2025. The review examines how AI has been incorporated into the control pipeline by organizing prior work according to functional roles, including perception and estimation, planning, learning-based control, interaction and safety, and learning and adaptation. In addition to this functional taxonomy, the study analyzes publication growth, application domains, robot types, evaluation settings, and methodological patterns to characterize the evolution of the field over the past decade. The results show that research activity has been concentrated primarily in learning control, while other functional roles have received comparatively less attention. The data also reveals an uneven distribution across application areas and robot platforms in the literature, with a strong reliance on simulation and limited evidence of integrated real-world deployment. These patterns indicate that, despite rapid growth and methodological diversity, the field remains imbalanced in both research focus and validation maturity. Rather than summarizing individual studies in isolation, this review provides a high-level perspective on where effort has been concentrated, where major gaps persist, and which directions are most critical for advancing AI-based robot manipulator control toward reliable and scalable real-world use. Full article
Show Figures

Figure 1

Back to TopTop