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Research and Application of Intelligent Control Algorithm, 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 10 September 2025 | Viewed by 637

Special Issue Editor


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Guest Editor
Science and Technology on Underwater Vehicle Laboratory, Harbin Engineering University, Harbin 15001, China
Interests: intelligent control; marine robot; formation system; distributed control technology; ship and ocean engineering; modeling and simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Intelligent control is a control mode involving intelligent information processing, intelligent information feedback, and intelligent control decision that represents an advanced stage in the development of control theory. Intelligent control is primarily used to solve the control problems of complex systems that are difficult to solve using traditional methods. Such systems commonly involve uncertain mathematical models and highly nonlinear and complex task requirements. How to combine intelligent control algorithms with various application scenarios has become the most challenging area in various research directions in the field of intelligent control algorithm application and engineering. Accordingly, new system description methods and intelligent control algorithms, such as motion control and task planning, are continually being developed to cope with different task scenarios and increasingly complex task requirements.

This Special Issue aims to focus on the recent advances in and challenges of intelligent control algorithms and applications in various fields. This Special Issue invites the submission of both review articles and original contributions on the theory, method, and application of intelligent control algorithms. Topics of interest include, but are not limited to, the following:

  • Mathematical modeling and framework of intelligent control systems;
  • Research on the theory and algorithm of intelligent control systems;
  • Simulation and test technology of intelligent control algorithms;
  • Application of intelligent control algorithms in robot systems;
  • Application of intelligent control algorithm in aerospace control systems;
  • Application of intelligent control algorithm in computer/modern integrated manufacturing system and computer/modern integrated operation system;
  • Application of intelligent control algorithm in transportation systems;
  • Other related topics.

Prof. Dr. Yanchao Sun
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. Applied Sciences 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 systems
  • robot systems
  • aerospace control systems
  • transportation systems
  • algorithm
  • simulation and test

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Published Papers (1 paper)

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Research

36 pages, 916 KiB  
Article
Fuzzy Control and Modeling Techniques Based on Multidimensional Membership Functions Defined by Fuzzy Clustering Algorithms
by Basil Mohammed Al-Hadithi and Javier Gómez
Appl. Sci. 2025, 15(8), 4479; https://doi.org/10.3390/app15084479 - 18 Apr 2025
Viewed by 131
Abstract
The increasing complexity of nonlinear multivariable systems poses significant challenges for effective modeling and control. Fuzzy modeling and control typically use fuzzy inference with one-dimensional membership functions. However, the use of multidimensional membership functions can provide significant benefits in optimizing and reducing the [...] Read more.
The increasing complexity of nonlinear multivariable systems poses significant challenges for effective modeling and control. Fuzzy modeling and control typically use fuzzy inference with one-dimensional membership functions. However, the use of multidimensional membership functions can provide significant benefits in optimizing and reducing the computational cost of a fuzzy controller. In this work, we propose the use of fuzzy clustering techniques to adjust and design multidimensional membership functions. These techniques represent a well-developed and comprehensive framework, though they are often disconnected from traditional fuzzy modeling and control methodologies. Thus, this work also seeks to combine fuzzy techniques of different applications with a single ultimate goal, namely, to optimize the modeling and control of nonlinear systems. Our main objective is system identification, modeling, and control using the Takagi–Sugeno method based on one-dimensional and multidimensional membership functions. Moreover, a comparison of various fuzzy clustering techniques for the design of multidimensional membership functions is carried out to demonstrate the effectiveness of the proposed methods in optimizing control performance and reducing computational cost. Full article
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