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New Trends in Fuzzy Control System Applications in Complex Industrial...
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New Trends in Fuzzy Control System Applications in Complex Industrial Processes and Energy Systems

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Process Control and Monitoring".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 5876

Special Issue Editors

Prof. Dr. Hua Chen

E-Mail Website
Guest Editor
School of Mathematics, Hohai University, Nanjing 210098, China
Interests: nonlinear control; fuzzy intelligent control; underactuated system
Special Issues, Collections and Topics in MDPI journals
Dr. Gang Wang

E-Mail Website
Guest Editor
Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai 200093, China
Interests: distributed control; adaptive control; robotics
Special Issues, Collections and Topics in MDPI journals
Dr. Yunfeng Ji

E-Mail Website
Guest Editor Assistant
Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai 200093, China
Interests: table tennis robot; motion control; image processing

Special Issue Information

Dear Colleagues,

With the growing complexity of modern industrial processes and energy systems, fuzzy control systems and intelligent control methods have become essential tools for addressing nonlinearities, disturbances, and time delays. By combining fuzzy logic, neural networks (NNs), and iterative learning control (ILC), these approaches enable innovative control strategies that significantly improve performance, stability, and energy efficiency across diverse applications. This Special Issue aims to gather pioneering research on both theoretical developments and practical implementations of these advanced control techniques, pushing the frontiers of intelligent control design in complex systems.

This Special Issue on “New Trends in Fuzzy Control System Applications in Complex Industrial Processes and Energy Systems” seeks high quality research focusing on the latest novel advances fuzzy/NN/ILC intelligent control and other nonlinear control methods for both theoretical analysis and practical applications. Topics include, but are not limited to, the following:

  • Intelligent control for nonlinear systems with time delay, disturbances, and a predetermined performance index for energy saving; 
  • Finite time/fixed time fuzzy control/NN control/ ILC/ model-free control design for the motion control  of several different types of robotics, such as USV, AUV, UAV, WMR, Table Tennis Robot, Inspection robot, and so on;
  • Distributed control/formation control/leader–follower tracking control design with fuzzy rule for nonlinear systems;
  • New LLM/video data/image processing technology, algorithm design, applications and modeling.

Prof. Dr. Hua Chen
Dr. Gang Wang
Guest Editors

Dr. Yunfeng Ji
Guest Editor Assistant

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

  • fuzzy control
  • nonlinear systems
  • robot
  • USV
  • time delay
  • predetermined performance index
  • LLM
  • video data
  • algorithms

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Published Papers (3 papers)

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Research

22 pages, 5335 KiB  
Article
Tuning of PID Controllers Using Reinforcement Learning for Nonlinear System Control
by Gheorghe Bujgoi and Dorin Sendrescu
Processes 2025, 13(3), 735; https://doi.org/10.3390/pr13030735 - 3 Mar 2025
Cited by 1 | Viewed by 2270
Abstract
This paper presents the application of reinforcement learning algorithms in the tuning of PID controllers for the control of some classes of continuous nonlinear systems. Tuning the parameters of the PID controllers is performed with the help of the Twin Delayed Deep Deterministic [...] Read more.
This paper presents the application of reinforcement learning algorithms in the tuning of PID controllers for the control of some classes of continuous nonlinear systems. Tuning the parameters of the PID controllers is performed with the help of the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which presents a series of advantages compared to other similar methods from machine learning dedicated to continuous state and action spaces. The TD3 algorithm is an off-policy actor–critic-based method and is used as it does not require a system model. Double Q-learning, delayed policy updates and target policy smoothing make TD3 robust against overestimation, increase its stability, and improve its exploration. These enhancements make TD3 one of the state-of-the-art algorithms for continuous control tasks. The presented technique is applied for the control of a biotechnological system that has strongly nonlinear dynamics. The proposed tuning method is compared to the classical tuning methods of PID controllers. The performance of the tuning method based on the TD3 algorithm is demonstrated through a simulation, illustrating the effectiveness of the proposed methodology. Full article
(This article belongs to the Special Issue New Trends in Fuzzy Control System Applications in Complex Industrial Processes and Energy Systems)
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15 pages, 3322 KiB  
Article
Development of a Fleet Management System for Multiple Robots’ Task Allocation Using Deep Reinforcement Learning
by Yanyan Dai, Deokgyu Kim and Kidong Lee
Processes 2024, 12(12), 2921; https://doi.org/10.3390/pr12122921 - 20 Dec 2024
Viewed by 1669
Abstract
This paper presents a fleet management system (FMS) for multiple robots, utilizing deep reinforcement learning (DRL) for dynamic task allocation and path planning. The proposed approach enables robots to autonomously optimize task execution, selecting the shortest and safest paths to target points. A [...] Read more.
This paper presents a fleet management system (FMS) for multiple robots, utilizing deep reinforcement learning (DRL) for dynamic task allocation and path planning. The proposed approach enables robots to autonomously optimize task execution, selecting the shortest and safest paths to target points. A deep Q-network (DQN)-based algorithm evaluates path efficiency and safety in complex environments, dynamically selecting the optimal robot to complete each task. Simulation results in a Gazebo environment demonstrate that Robot 2 achieved a path 20% shorter than other robots while successfully completing its task. Training results reveal that Robot 1 reduced its cost by 50% within the first 50 steps and stabilized near-optimal performance after 1000 steps, Robot 2 converged after 4000 steps with minor fluctuations, and Robot 3 exhibited steep cost reduction, converging after 10,000 steps. The FMS architecture includes a browser-based interface, Node.js server, rosbridge server, and ROS for robot control, providing intuitive monitoring and task assignment capabilities. This research demonstrates the system’s effectiveness in multi-robot coordination, task allocation, and adaptability to dynamic environments, contributing significantly to the field of robotics. Full article
(This article belongs to the Special Issue New Trends in Fuzzy Control System Applications in Complex Industrial Processes and Energy Systems)
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18 pages, 877 KiB  
Article
Intelligent Model-Free Control for Power Line Inspection Robots: Tackling Input Time Delays with Data-Driven Solutions
by Nan Zhang, Jingyi Su, Jiahui Huang, Xinyuan Long and Hua Chen
Processes 2024, 12(11), 2430; https://doi.org/10.3390/pr12112430 - 4 Nov 2024
Viewed by 1034
Abstract
This article presents an innovative approach to model-free adaptive control designed for power line inspection robots facing challenges with input time delays. The strategy begins by employing a compact-form dynamic linearization technique to transform the original system into a data-driven model. Subsequently, utilizing [...] Read more.
This article presents an innovative approach to model-free adaptive control designed for power line inspection robots facing challenges with input time delays. The strategy begins by employing a compact-form dynamic linearization technique to transform the original system into a data-driven model. Subsequently, utilizing real-time input and output information, the system’s pseudo-partial derivatives are assessed online. Leveraging these assessment parameters, a weighted one-step prediction control mechanism is designed, and a compact-form dynamic linearization model-free adaptive control framework is established. Moreover, the research incorporates compression mapping to thoroughly confirm the convergence of the algorithm, thereby ensuring its stability. Ultimately, the effectiveness and practicality of this control method are substantiated through a series of simulation experiments, demonstrating its robust performance. Full article
(This article belongs to the Special Issue New Trends in Fuzzy Control System Applications in Complex Industrial Processes and Energy Systems)
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