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Processes
Special Issues
Process Control Technologies and Their Applications in Industry
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Process Control Technologies and Their Applications in Industry

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".

Deadline for manuscript submissions: 30 October 2024 | Viewed by 841

Special Issue Editor

Dr. Luping Zhao

E-Mail Website
Guest Editor
College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
Interests: model predictive control; optimization algorithms; machine learning-based control; statistical process control

Special Issue Information

Dear Colleagues,

Advanced control technologies have revolutionized several industrial sectors by enabling enhanced process optimization, increased efficiency, and improved product quality. This Special Issue delves into the latest developments in advanced control technologies and their wide-ranging applications in different industries. This field encompasses a diverse range of methodologies, from model predictive control (MPC) and adaptive control to advanced optimization algorithms and machine learning-based control systems. The integration of these cutting-edge control technologies in industries such as manufacturing, energy, chemical processing, and transportation promises to unlock new levels of operational excellence and performance. This Special Issue explores the potential of advanced control technologies to drive innovation, enhance productivity, and address complex control challenges across various industrial domains.

Overall, the goal of this Special Issue is to showcase the latest advancements in control technologies and their diverse applications across different industries. Whether your research focuses on model predictive control, adaptive control, optimization algorithms, or machine learning-based control systems, we encourage you to share your valuable insights and findings. We look forward to receiving your contributions and fostering meaningful discussions in this field.

Dr. Luping Zhao
Guest Editor

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. Processes is an international peer-reviewed open access monthly 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

  • advanced control technologies
  • model predictive control
  • adaptive control
  • optimization algorithms
  • machine learning-based control

Published Papers (1 paper)

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Research

27 pages, 6054 KiB  
Article
Layered Composite Decoupling Control Based on Regional Dynamic Sparrow Search Algorithm
by Bo Fu, Bijia You and Guozhen Hu
Processes 2023, 11(12), 3350; https://doi.org/10.3390/pr11123350 - 01 Dec 2023
Viewed by 590
Abstract
This paper addresses the issues of coupling and disturbances in a dual-tank water level control system within the context of process control in chemical water treatment at industrial facilities. In response to these challenges, a Layered Composite Decoupling Control system based on the [...] Read more.
This paper addresses the issues of coupling and disturbances in a dual-tank water level control system within the context of process control in chemical water treatment at industrial facilities. In response to these challenges, a Layered Composite Decoupling Control system based on the Regional Dynamic Sparrow Search Algorithm (RDSSA-LCDC) is proposed. The utilization of an enhanced Regionally Dynamic Sparrow Search Algorithm (RDSSA) addresses the pitfalls of the Sparrow Search Algorithm (SSA), such as susceptibility to local optima and inadequate precision. RDSSA is employed for the parameter tuning of the system’s PID controller. Structurally, it incorporates a Hierarchical Composite Decoupling Control (LCDC) strategy, initially establishing a forward channel to construct an inner-layer decoupling model employing pre-feedback to rectify the lower-level system’s inputs, thereby mitigating inter-branch coupling. Subsequently, it develops an improved disturbance observer model based on pseudo-inverse compensation in the feedback channel, addressing conventional disturbance observer biases, and observing and suppressing system coupling and disturbances. Finally, within the dual-tank water level control system, various control schemes are simulated and compared, affirming the approach’s commendable decoupling, responsiveness, and disturbance rejection performance. Full article
(This article belongs to the Special Issue Process Control Technologies and Their Applications in Industry)
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