Application of Artificial Intelligence and Control Theory

A special issue of Mathematics (ISSN 2227-7390).

Deadline for manuscript submissions: closed (31 July 2023) | Viewed by 1062

Special Issue Editors


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Guest Editor
Department of Ship Automation, Faculty of Electrical Engineering, Gdynia Maritime University, Gdynia, Poland
Interests: artificial intelligence; control engineering; machine learning; fuzzy logic; static and dynamic optimization; model predictive control; power electronics

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Guest Editor
Department of Ship Automation, Faculty of Marine Electrical Engineering, Gdynia Maritime University, 83 Morska Str., 81-225 Gdynia, Poland
Interests: control engineering; optimization; differential games; artificial intelligence; sensitivity of control; remote sensing; technology development; applications
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Special Issue Information

Dear Colleagues,

In recent years, there has been a rapid increase in the use of algorithms based on artificial intelligence to solve many problems and issues in various areas of human life. Indeed, artificial intelligence will enable computers to perceive and interact with their own surroundings based on real data collection, processing and analyzing the data to solve specific problems related to human life, with the main objective of achieving well-designed goals and reacting accordingly in real environments. In this context, the combination of artificial intelligence with control theory, which is an interdisciplinary branch of engineering and mathematics, will provide a very promising solution for various engineering and technological tasks related to human life. In this Special Issue, we invite you to publish your innovative approaches and ideas in this field. Thank you in advance for participating in this project that focuses on the application of innovative techniques and the design of new algorithms and covers the following topics:

  • Artificial intelligence;
  • Particle swarm optimization;
  • Artificial swarm intelligence;
  • Artificial neural networks;
  • Machine learning;
  • Fuzzy set theory;
  • Genetic algorithm;
  • Linear and nonlinear control theory;
  • Optimal control;
  • Model predictive control;
  • Adaptive control;
  • Game theory application;
  • Sensitivity of control.

Dr. Mostefa Mohamed-Seghir
Prof. Dr. Józef Lisowski
Guest Editors

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • particle swarm optimization
  • artificial swarm intelligence
  • artificial neural networks
  • machine learning
  • fuzzy set theory
  • genetic algorithm
  • linear and nonlinear control theory
  • optimal control
  • model predictive control
  • adaptive control
  • game theory application
  • sensitivity of control

Published Papers (1 paper)

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Research

19 pages, 5262 KiB  
Article
Feature Selection Fuzzy Neural Network Super-Twisting Harmonic Control
by Qi Pan, Yanli Zhou and Juntao Fei
Mathematics 2023, 11(6), 1495; https://doi.org/10.3390/math11061495 - 18 Mar 2023
Cited by 1 | Viewed by 764
Abstract
This paper provides a multi-feedback feature selection fuzzy neural network (MFFSFNN) based on super-twisting sliding mode control (STSMC), aiming at compensating for current distortion and solving the harmonic current problem in an active power filter (APF) system. A feature selection layer is added [...] Read more.
This paper provides a multi-feedback feature selection fuzzy neural network (MFFSFNN) based on super-twisting sliding mode control (STSMC), aiming at compensating for current distortion and solving the harmonic current problem in an active power filter (APF) system. A feature selection layer is added to an output feedback neural network to attach the characteristics of signal filtering to the neural network. MFFSFNN, with the designed feedback loops and hidden layer, has the advantages of signal judging, filtering, and feedback. Signal filtering can choose valuable signals to deal with lumped uncertainties, and signal feedback can expand the learning dimension to improve the approximation accuracy. The STSMC, as a compensator with adaptive gains, helps to stabilize the compensation current. An experimental study is implemented to prove the effectiveness and superiority of the proposed controller. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence and Control Theory)
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