Dynamic Modeling and Simulation for Control Systems, 3rd Edition

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

Deadline for manuscript submissions: 10 April 2025 | Viewed by 1687

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Department of Robotics and Production Systems, University Politehnica of Bucharest, 060042 Bucharest, Romania
Interests: robotics; dynamic behavior; neural networks; mobile robots; neurorehabilitation
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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
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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
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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

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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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Published Papers (1 paper)

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Research

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
Viewed by 474
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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