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Mathematics
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Modeling and Simulation of Control System
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Modeling and Simulation of Control System

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E2: Control Theory and Mechanics".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 8460

Special Issue Editor

Dr. Krzysztof Foit

E-Mail Website
Guest Editor
Department Of Engineering Processes Automation And Integrated Manufacturing Systems, Silesian University of Technology, 44-100 Gliwice, Poland
Interests: mechanical engineering; robotics; control systems; manufacturing; computer science; artificial intelligence

Special Issue Information

Dear Colleagues,

It is difficult to imagine the world without control systems that are a crucial part of almost all modern devices. The development of science and technology brings new challenges in the field of designing such systems; however, due to the growing importance of digital twins, the creation of accurate models as well as the search for new simulation methods is currently the essential path in the modeling and simulation of real systems.

The aim of this Special Issue is to collect papers dealing with new trends in the modeling and simulation of control systems. I would like to warmly invite researchers involved in this broad area to contribute to this issue by submitting original research papers. The scope of scientific problems includes mathematical theory, development of methodologies, presentation of new concepts, methods, and ideas, and applications of modeling and simulation of control systems. Papers presenting theoretical aspects concerning modeling and simulation, as well as those describing practical applications of methods, are welcome. All submitted work will be subject to a review process. The published papers will be an excellent source of information on the current state of knowledge, and I also hope that they will become an inspiration for further research.

The subject areas of this Special Issue focus on the modeling and simulation of control systems and include (but are not limited to) the following fields:

  • Industry 4.0;
  • Internet of Things;
  • Manufacturing and smart manufacturing;
  • Automotive control systems;
  • Autonomous vehicles;
  • Robotics (industrial robots, mobile robots, cobots, social robots, etc.);
  • Medical equipment;
  • Smart buildings.

Dr. Krzysztof Foit
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. Mathematics 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 2600 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

  • mathematical method
  • mathematical model
  • engineering mathematics
  • modelling
  • simulation
  • control system
  • automation

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Published Papers (3 papers)

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Research

34 pages, 15176 KiB  
Article
Improvement of Linear and Nonlinear Control for PMSM Using Computational Intelligence and Reinforcement Learning
by Marcel Nicola and Claudiu-Ionel Nicola
Mathematics 2022, 10(24), 4667; https://doi.org/10.3390/math10244667 - 9 Dec 2022
Cited by 10 | Viewed by 2300
Abstract
Starting from the nonlinear operating equations of the permanent magnet synchronous motor (PMSM) and from the global strategy of the field-oriented control (FOC), this article compares the linear and nonlinear control of a PMSM. It presents the linear quadratic regulator (LQR) algorithm as [...] Read more.
Starting from the nonlinear operating equations of the permanent magnet synchronous motor (PMSM) and from the global strategy of the field-oriented control (FOC), this article compares the linear and nonlinear control of a PMSM. It presents the linear quadratic regulator (LQR) algorithm as a linear control algorithm, in addition to that obtained through feedback linearization (FL). Naturally, the nonlinear approach through the Lyapunov and Hamiltonian functions leads to results that are superior to those of the linear algorithms. With the particle swarm optimization (PSO), simulated annealing (SA), genetic algorithm (GA), and gray wolf Optimization (GWO) computational intelligence (CI) algorithms, the performance of the PMSM–control system (CS) was optimized by obtaining parameter vectors from the control algorithms by optimizing specific performance indices. Superior performance of the PMSM–CS was also obtained by using reinforcement learning (RL) algorithms, which provided correction command signals (CCSs) after the training stages. Starting from the PMSM–CS performance that was obtained for a benchmark, there were four types of linear and nonlinear control algorithms for the control of a PMSM, together with the means of improving the PMSM–CS performance by using CI algorithms and RL–twin delayed deep deterministic policy gradient (TD3) agent algorithms. The article also presents experimental results that confirm the superiority of PMSM–CS–CI over classical PI-type controllers. Full article
(This article belongs to the Special Issue Modeling and Simulation of Control System)
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32 pages, 28937 KiB  
Article
Bearing Fault Diagnosis for an Induction Motor Controlled by an Artificial Neural Network—Direct Torque Control Using the Hilbert Transform
by Abderrahman El Idrissi, Aziz Derouich, Said Mahfoud, Najib El Ouanjli, Ahmed Chantoufi, Ameena Saad Al-Sumaiti and Mahmoud A. Mossa
Mathematics 2022, 10(22), 4258; https://doi.org/10.3390/math10224258 - 14 Nov 2022
Cited by 24 | Viewed by 2450
Abstract
Motor Current Signature Analysis (MCSA) is a popular method for the detection of faults in electric motor drives, particularly in Induction Machines (IMs). For Bearing Defects (BDs), which are very much related to the rotational frequency, it is important to maintain the speed [...] Read more.
Motor Current Signature Analysis (MCSA) is a popular method for the detection of faults in electric motor drives, particularly in Induction Machines (IMs). For Bearing Defects (BDs), which are very much related to the rotational frequency, it is important to maintain the speed at a target reference value in order to distinguish and locate the different BDs. This can be achieved by using a powerful control such as the Direct Torque Control (DTC), but this control causes the variation of the supply frequency and the current signal to become non-stationary, so the integration of advanced signal processing methods becomes necessary by using a suitable filter to handle the frequency content depending on the BDs, such as the Hilbert filter. This paper aims to adopt the Hilbert Transform (HT) for extracting the signature of the faults from the stator current envelope to detect the different BDs in the IMs when they are controlled by an intelligent DTC control driven by Artificial Neural Networks (ANN-DTC). This ANN-DTC control is a shaping factor rather than a disturbing one, which contributes with the Hilbert filter to the diagnosis of BDs. This technique is tested for the four locations of BDs: the inner ring, the outer ring, the ball, and the bearing cage in different operating situations without control and with conventional DTC and ANN-DTC controls. Thus, detecting the location of the defect exactly at an early stage contributes to achieving maintenance in a fairly short time. The performance of the chosen approach lies in minimizing the electromagnetic torque ripples as a result of the control and increase of the amplitudes of the spectra related to BDs compared to other harmonics. This performance is verified in the MATLAB/SIMULINK environment. Full article
(This article belongs to the Special Issue Modeling and Simulation of Control System)
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22 pages, 21722 KiB  
Article
Artificial Neural Based Speed and Flux Estimators for Induction Machine Drives with Matlab/Simulink
by Ahmed A. Zaki Diab, Mohammed A. Elsawy, Kotin A. Denis, Salem Alkhalaf and Ziad M. Ali
Mathematics 2022, 10(8), 1348; https://doi.org/10.3390/math10081348 - 18 Apr 2022
Cited by 6 | Viewed by 3006
Abstract
In this paper, an Artificial Neural Network (ANN) for accurate estimation of the speed and flux for induction motor (IM) drives has been presented for industrial applications such as electric vehicles (EVs). Two ANN estimators have been designed, one for the rotor speed [...] Read more.
In this paper, an Artificial Neural Network (ANN) for accurate estimation of the speed and flux for induction motor (IM) drives has been presented for industrial applications such as electric vehicles (EVs). Two ANN estimators have been designed, one for the rotor speed estimation and the other for the stator and rotor flux estimation. The input training data has been collected based on the currents and voltage data, while the output training data of the speed and stator and rotor fluxes has been established based on the measured speed and flux estimator-based mathematical model of the IM. The designed ANN estimators can overcome the problem of the parameter’s variations and drift integration problems. Matlab/Simulink has been used to develop and test the ANN estimators. The results prove the ANN estimators’ effectiveness under various operation conditions. Full article
(This article belongs to the Special Issue Modeling and Simulation of Control System)
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