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Mathematics
Special Issues
Modeling, Simulation and Control of Dynamical Systems
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Modeling, Simulation and Control of Dynamical Systems

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Dynamical Systems".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 1071

Special Issue Editors

Dr. Humam Al-Baidhani

E-Mail Website
Guest Editor
Department of Electrical Engineering, Wright State University, Dayton, OH 45435, USA
Interests: DC-DC converters; modeling and control; nonlinear systems
Prof. Dr. Marian K. Kazimierczuk

E-Mail Website
Guest Editor
Department of Electrical Engineering, Wright State University, Dayton, OH 45435-0001, USA
Interests: modeling and control; resonant converters; RF power amplifiers; magnetics; industrial electronics

Special Issue Information

Dear Colleagues,

Dynamical systems arise in various engineering fields, including electrical, mechanical, civil, and control engineering areas. In order to study and characterize the systems that involve dynamic processes, mathematical modeling and simulation are required. Such engineering tools play a vital role in understanding the system behavior, designing a suitable control law, and analyzing the system’s stability. The design of proper control schemes is essential to track the desired trajectory and improve the system dynamics. However, in real-world applications, the nonlinearities, modeling uncertainties, and external disturbances can complicate the modeling and control design tasks. Hence, advanced approaches are required to design robust control schemes and provide rigorous analysis to ensure the stability of the closed-loop control systems.

In this context, this Special Issue aims to collect state-of-the-art research contributions on the modeling, simulation, and control of dynamical systems. Accurate models and advanced control techniques of nonlinear systems that resolve practical control design issues are encouraged.

Potential topics include, but are not limited to, the following:

  1. Stability analysis of dynamical systems;
  2. Real-time and HIL simulation; 
  3. Control and analysis of nonlinear systems;
  4. Modeling and control of power converters;
  5. Digital control system design and applications;
  6. Artificial intelligence in controls and robotics;
  7. Tracking and disturbance rejection enhancement;
  8. Design and optimization of PV systems;
  9. Simulation and control in aerospace.

Dr. Humam Al-Baidhani
Prof. Dr. Marian K. Kazimierczuk
Guest Editors

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

  • control systems
  • modeling and simulation
  • nonlinear systems
  • stability analysis

Published Papers (2 papers)

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Research

15 pages, 4676 KiB  
Article
Numerical Investigation of Supersonic Flow over a Wedge by Solving 2D Euler Equations Utilizing the Steger–Warming Flux Vector Splitting (FVS) Scheme
by Mitch Wolff, Hashim H. Abada and Hussein Awad Kurdi Saad
Mathematics 2024, 12(9), 1282; https://doi.org/10.3390/math12091282 - 24 Apr 2024
Viewed by 332
Abstract
Supersonic flow over a half-angle wedge (θ = 15°) with an upstream Mach number of 2.0 was investigated using 2D Euler equations where sea level conditions were considered. The investigation employed the Steger–Warming flux vector splitting (FVS) method executed in MATLAB 9.13.0 (R2022b) [...] Read more.
Supersonic flow over a half-angle wedge (θ = 15°) with an upstream Mach number of 2.0 was investigated using 2D Euler equations where sea level conditions were considered. The investigation employed the Steger–Warming flux vector splitting (FVS) method executed in MATLAB 9.13.0 (R2022b) software. The study involved a meticulous comparison between theoretical calculations and numerical results. Particularly, the research emphasized the angle of oblique shock and downstream flow properties. A substantial iteration count of 2000 iteratively refined the outcomes, underscoring the role of advanced computational resources. Validation and comparative assessment were conducted to elucidate the superiority of the Steger–Warming flux vector splitting (FVS) scheme over existing methodologies. This research serves as a link between theoretical rigor and practical applications in high-speed aerospace design, enhancing the efficiency of aircraft components. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Dynamical Systems)
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22 pages, 1024 KiB  
Article
Reinforcement Learning-Based Control of a Power Electronic Converter
by Dajr Alfred, Dariusz Czarkowski and Jiaxin Teng
Mathematics 2024, 12(5), 671; https://doi.org/10.3390/math12050671 - 25 Feb 2024
Viewed by 554
Abstract
This article presents a modern, data-driven, reinforcement learning-based (RL-based), discrete-time control methodology for power electronic converters. Additionally, the key advantages and disadvantages of this novel control method in comparison to classical frequency-domain-derived PID control are examined. One key advantage of this technique is [...] Read more.
This article presents a modern, data-driven, reinforcement learning-based (RL-based), discrete-time control methodology for power electronic converters. Additionally, the key advantages and disadvantages of this novel control method in comparison to classical frequency-domain-derived PID control are examined. One key advantage of this technique is that it obviates the need to derive an accurate system/plant model by utilizing measured data to iteratively solve for an optimal control solution. This optimization algorithm stems from the linear quadratic regulator (LQR) and involves the iterative solution of an algebraic Riccati equation (ARE). Simulation results implemented on a buck converter are provided to verify the effectiveness and examine the limitations of the proposed control strategy. The implementation of a classical Type-III compensator was also simulated to serve as a performance comparison to the proposed controller. Full article
(This article belongs to the Special Issue Modeling, Simulation and Control of Dynamical Systems)
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