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Dynamic Modeling and Simulation for Control Systems, 3rd Edition
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Dynamic Modeling and Simulation for Control Systems, 3rd Edition

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 (10 April 2025) | Viewed by 5539

Special Issue Editors

Prof. Dr. Adrian Olaru

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Guest Editor
Department of Robotics and Production Systems, University Politehnica of Bucharest, 060042 Bucharest, Romania
Interests: robotics; dynamic behavior; neural networks; mobile robots; neurorehabilitation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gabriel Frumusanu

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Guest Editor
Manufacturing Science and Engineering Department, “Dunarea de Jos” University of Galati, 800201 Galati, Romania
Interests: numerical modeling of machining systems; manufacturing process control; dynamics of cutting processes; chaos theory; computer-assisted design
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Catalin Alexandru

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Guest Editor
Department of Product Design, Mechatronics and Environment, Transilvania University of Brasov, 500036 Brasov, Romania
Interests: mechanical systems; renewable energy systems; virtual prototyping; modeling and simulation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue titled “Dynamic Modeling and Simulation for Control Systems, Third Edition”, as a follow-up to the successful first edition and second edition, will provide a comprehensive platform for researchers and practitioners to explore topics related to the dynamic modeling, simulation, and optimization of control systems in various engineering fields. Specifically, this Special Issue aims to cover important aspects of how to optimize the dynamic behavior of physical systems using special algorithms and artificial intelligence in the modeling, simulation, and optimization of the components and systems across diverse engineering disciplines, such as astronautics, aerospace, avionics, robotics, manufacturing systems, mechanical engineering, power energy, materials technology, and neurorehabilitation.

Topics for this Special Issue:

  • Mathematical modeling of control systems;
  • Control of physical engineering systems;
  • Optimization algorithms in engineering systems;
  • Design of physical engineering systems;
  • Mechanical, electrical, and fluid interaction between system components;
  • Dynamic behavior analysis;
  • System response analysis;
  • Feedback control systems;
  • Numerical simulation of integrated systems;
  • Fault detection and diagnosis;
  • Networked control and time-delay systems;
  • Frequency response and stability;
  • Control and simulation of the isotope separation process;
  • Fuzzy logic and control systems;
  • Neural network applied in complex control systems;
  • Artificial intelligence and support vector machine for control systems.

This Special Issue of Mathematics will be a useful guide on techniques for the modeling, simulation, and optimization of control systems to obtain acceptable dynamic behaviors.

Prof. Dr. Adrian Olaru
Prof. Dr. Gabriel Frumusanu
Prof. Dr. Catalin Alexandru
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

  • mathematical modeling
  • numerical simulation
  • optimization algorithms
  • control systems
  • time response analysis
  • time-delay systems
  • feedback control
  • networked control
  • stochastic control
  • fault detection
  • robust control
  • adaptive control
  • frequency response analysis
  • stability analysis
  • fuzzy logic
  • data acquisition
  • neural networks
  • artificial intelligence
  • mechanical and electrical interaction
  • physical engineering design

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Related Special Issue

Published Papers (5 papers)

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Research

28 pages, 4927 KiB  
Article
Hybrid Genetic Algorithm-Based Optimal Sizing of a PV–Wind–Diesel–Battery Microgrid: A Case Study for the ICT Center, Ethiopia
by Adnan Kedir Jarso, Ganggyoo Jin and Jongkap Ahn
Mathematics 2025, 13(6), 985; https://doi.org/10.3390/math13060985 - 17 Mar 2025
Viewed by 165
Abstract
This study presents analysis and optimization of a standalone hybrid renewable energy system (HRES) for Adama Science and Technology University’s ICT center in Ethiopia. The proposed hybrid system combines photovoltaic panels, wind turbines, a battery bank, and a diesel generator to ensure reliable [...] Read more.
This study presents analysis and optimization of a standalone hybrid renewable energy system (HRES) for Adama Science and Technology University’s ICT center in Ethiopia. The proposed hybrid system combines photovoltaic panels, wind turbines, a battery bank, and a diesel generator to ensure reliable and sustainable power. The objectives are to minimize the system’s total annualized cost and loss of power supply probability, while energy reliability is maintained. To optimize the component sizing and energy management strategy of the HRES, we formulated a mathematical model that incorporates the variability of renewable energy and load demand. This optimization problem is solved using a hybrid genetic algorithm (HGA). Simulation results indicate that the HGA yielded the best solution, characterized by the levelized cost of energy of USD 0.2546/kWh, the loss of power supply probability of 0.58%, and a convergence time of 197.2889 s. Full article
(This article belongs to the Special Issue Dynamic Modeling and Simulation for Control Systems, 3rd Edition)
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19 pages, 3164 KiB  
Article
Application of Real-Coded Genetic Algorithm–PID Cascade Speed Controller to Marine Gas Turbine Engine Based on Sensitivity Function Analysis
by Yunhyung Lee, Kitak Ryu, Gunbaek So, Jaesung Kwon and Jongkap Ahn
Mathematics 2025, 13(2), 314; https://doi.org/10.3390/math13020314 - 19 Jan 2025
Viewed by 589
Abstract
Gas turbine engines at sea, characterized by nonlinear behavior and parameter variations due to dynamic marine environments, pose challenges for precise speed control. The focus of this study was a COGAG system with four LM-2500 gas turbines. A third-order model with time delay [...] Read more.
Gas turbine engines at sea, characterized by nonlinear behavior and parameter variations due to dynamic marine environments, pose challenges for precise speed control. The focus of this study was a COGAG system with four LM-2500 gas turbines. A third-order model with time delay was derived at three operating points using commissioning data to capture the engines’ inherent characteristics. The cascade controller design employs a real-coded genetic algorithm–PID (R-PID) controller, optimizing PID parameters for each model. Simulations revealed that the R-PID controllers, optimized for robustness, show Nyquist path stability, maintaining the furthest distance from the critical point (−1, j0). The smallest sensitivity function Ms (maximum sensitivity) values and minimal changes in Ms for uncertain plants confirm robustness against uncertainties. Comparing transient responses, the R-PID controller outperforms traditional methods like IMC and Sadeghi in total variation in control input, settling time, overshoot, and ITAE, despite a slightly slower rise time. However, controllers designed for specific operating points show decreased performance when applied beyond those points, with increased rise time, settling time, and overshoot, highlighting the need for operating-point-specific designs to ensure optimal performance. This research underscores the importance of tailored controller design for effective gas turbine engine management in marine applications. Full article
(This article belongs to the Special Issue Dynamic Modeling and Simulation for Control Systems, 3rd Edition)
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17 pages, 949 KiB  
Article
Adaptive Control for Multi-Agent Systems Governed by Fractional-Order Space-Varying Partial Integro-Differential Equations
by Zhen Liu, Yingying Wen, Bin Zhao and Chengdong Yang
Mathematics 2025, 13(1), 112; https://doi.org/10.3390/math13010112 - 30 Dec 2024
Viewed by 617
Abstract
This paper investigates a class of multi-agent systems (MASs) governed by nonlinear fractional-order space-varying partial integro-differential equations (SVPIDEs), which incorporate both nonlinear state terms and integro terms. Firstly, a distributed adaptive control protocol is developed for leaderless fractional-order SVPIDE-based MASs, aiming to achieve [...] Read more.
This paper investigates a class of multi-agent systems (MASs) governed by nonlinear fractional-order space-varying partial integro-differential equations (SVPIDEs), which incorporate both nonlinear state terms and integro terms. Firstly, a distributed adaptive control protocol is developed for leaderless fractional-order SVPIDE-based MASs, aiming to achieve consensus among all agents without a leader. Then, for leader-following fractional-order SVPIDE-based MASs, the protocol is extended to account for communication between the leader and follower agents, ensuring that the followers reach consensus with the leader. Finally, three examples are presented to illustrate the effectiveness of the proposed distributed adaptive control protocols. Full article
(This article belongs to the Special Issue Dynamic Modeling and Simulation for Control Systems, 3rd Edition)
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13 pages, 1145 KiB  
Article
Distributed Bipartite Consensus of Multi-Agent Systems via Disturbance Rejection Control Strategy
by Subramanian Manickavalli, Arumugam Parivallal, Ramasamy Kavikumar and Boomipalagan Kaviarasan
Mathematics 2024, 12(20), 3225; https://doi.org/10.3390/math12203225 - 15 Oct 2024
Viewed by 1120
Abstract
This work aims to focus on analyzing the consensus control problem in cooperative–competitive networks in the occurrence of external disturbances. The primary motive of this work is to employ the equivalent input-disturbance estimation technique to compensate for the impact of external disturbances in [...] Read more.
This work aims to focus on analyzing the consensus control problem in cooperative–competitive networks in the occurrence of external disturbances. The primary motive of this work is to employ the equivalent input-disturbance estimation technique to compensate for the impact of external disturbances in the considered multi-agent system. In particular, a suitable low-pass filter is implemented to enhance the accuracy of disturbance estimation performance. In addition, a specific signed, connected, and structurally balanced undirected communication graph with positive and negative edge weights is considered to express the cooperation–competition communication among neighboring agents. The cooperative–competitive multi-agent system reaches its final state with same magnitude and in opposite direction under the considered structurally balanced graph. By utilizing the properties of Lyapunov stability theory and graph theory, the adequate conditions assuring the bipartite consensus of the examined multi-agent system are established as linear matrix inequalities. An illustrative example is delivered at the end to check the efficacy of the designed control scheme. Full article
(This article belongs to the Special Issue Dynamic Modeling and Simulation for Control Systems, 3rd Edition)
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17 pages, 3678 KiB  
Article
Electric Vehicle Motor Fault Detection with Improved Recurrent 1D Convolutional Neural Network
by Prashant Kumar, Prince, Ashish Kumar Sinha and Heung Soo Kim
Mathematics 2024, 12(19), 3012; https://doi.org/10.3390/math12193012 - 26 Sep 2024
Cited by 2 | Viewed by 1297
Abstract
The reliability of electric vehicles (EVs) is crucial for the performance and safety of modern transportation systems. Electric motors are the driving force in EVs, and their maintenance is critical for efficient EV performance. The conventional fault detection methods for motors often struggle [...] Read more.
The reliability of electric vehicles (EVs) is crucial for the performance and safety of modern transportation systems. Electric motors are the driving force in EVs, and their maintenance is critical for efficient EV performance. The conventional fault detection methods for motors often struggle with accurately capturing complex spatiotemporal vibration patterns. This paper proposes a recurrent convolutional neural network (RCNN) for effective defect detection in motors, taking advantage of the advances in deep learning techniques. The proposed approach applies long short-term memory (LSTM) layers to capture the temporal dynamics essential for fault detection and convolutional neural network layers to mine local features from the segmented vibration data. This hybrid method helps the model to learn complicated representations and correlations within the data, leading to improved fault detection. Model development and testing are conducted using a sizable dataset that includes various kinds of motor defects under differing operational scenarios. The results demonstrate that, in terms of fault detection accuracy, the proposed RCNN-based strategy performs better than the traditional fault detection techniques. The performance of the model is assessed under varying vibration data noise levels to further guarantee its effectiveness in practical applications. Full article
(This article belongs to the Special Issue Dynamic Modeling and Simulation for Control Systems, 3rd Edition)
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