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Mathematics
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Control, Modeling, Analysis, Optimization and Applications in Mechanical Engineering
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Control, Modeling, Analysis, Optimization and Applications in Mechanical Engineering

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 (31 December 2024) | Viewed by 5437

Special Issue Editors

Prof. Dr. José Luis Olazagoitia

E-Mail Website
Guest Editor
Faculty of Design and Technology, UDIT—Universidad de Diseño y Tecnología, 28016 Madrid, Spain
Interests: industrial robotics; computational simulation; mechanical engineering; noise and vibration; energy harvesting; low cost electronics and application; vehicle engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jordi Vinolas

E-Mail Website
Guest Editor
Industrial Engineering, Universidad Francisco de Vitoria, 28925 Madrid, Spain
Interests: machine dynamics; noise and vibration; railway dynamics and energy harvesting

Special Issue Information

Dear Colleagues,

I am pleased to announce the launch of a Special Issue for an important scientific topic on "Control, Modeling, Analysis, Optimization and Applications in Mechanical Engineering" for Mathematics. This Special Issue aims to explore the latest advancements in the application of mathematical techniques to control, modeling, analysis, and optimization in mechanical engineering.

Mechanical engineering is an interdisciplinary field that deals with the design, analysis, and manufacturing of mechanical systems. The use of mathematical techniques such as linear algebra, calculus, differential equations, optimization theory, and numerical methods has become increasingly important in recent years, enabling engineers to solve complex problems and optimize designs. In addition to these techniques, the latest advances in artificial intelligence and machine learning have provided new and exciting opportunities for solving problems in mechanical engineering.

We welcome original research papers, review articles, and short communications related to this theme, covering the application of mathematical techniques to areas such as:

  • Mathematical modeling and simulation of mechanical systems.
  • Control theory and applications in mechanical systems.
  • Optimization techniques for mechanical systems.
  • Structural analysis and design of mechanical systems.
  • Thermal analysis and design of mechanical systems.
  • Computational fluid dynamics in mechanical engineering applications.
  • Robotics and automation in mechanical engineering applications.
  • Additive manufacturing in mechanical engineering applications.

All submissions will undergo rigorous peer-review by experts in the field, with manuscripts evaluated on the basis of their originality, significance, and scientific quality.

We encourage submissions from both academia and industry, and we believe that this Special Issue will provide a valuable platform for the sharing of knowledge, exchange of ideas, and development of collaborations among researchers, practitioners, and professionals in the field of mechanical engineering.

We look forward to your valuable contributions.

Prof. Dr. José Luis Olazagoitia
Prof. Dr. Jordi Vinolas
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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

15 pages, 2902 KiB  
Article
Modeling the Dynamics of Prosthetic Fingers for the Development of Predictive Control Algorithms
by José Vicente García-Ortíz, Marta C. Mora and Joaquín Cerdá-Boluda
Mathematics 2024, 12(20), 3236; https://doi.org/10.3390/math12203236 - 16 Oct 2024
Viewed by 1632
Abstract
In the field of biomechanical modeling, the development of a prosthetic hand with dexterity comparable to the human hand is a multidisciplinary challenge involving complex mechatronic systems, intuitive control schemes, and effective body interfaces. Most current commercial prostheses offer limited functionality, typically only [...] Read more.
In the field of biomechanical modeling, the development of a prosthetic hand with dexterity comparable to the human hand is a multidisciplinary challenge involving complex mechatronic systems, intuitive control schemes, and effective body interfaces. Most current commercial prostheses offer limited functionality, typically only one or two degrees of freedom (DoF), resulting in reduced user adoption due to discomfort and lack of functionality. This research aims to design a computationally efficient low-level control algorithm for prosthetic hand fingers to be able to (a) accurately manage finger positions, (b) anticipate future information, and (c) minimize power consumption. The methodology employed is known as model-based predictive control (MBPC) and starts with the application of linear identification techniques to model the system dynamics. Then, the identified model is used to implement a generalized predictive control (GPC) algorithm, which optimizes the control effort and system performance. A test bench is used for experimental validation, and the results demonstrate that the proposed control scheme significantly improves the prosthesis’ dexterity and energy efficiency, enhancing its potential for daily use by people with hand loss. Full article
(This article belongs to the Special Issue Control, Modeling, Analysis, Optimization and Applications in Mechanical Engineering)
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21 pages, 9096 KiB  
Article
Working Performance Improvement of a Novel Independent Metering Valve System by Using a Neural Network-Fractional Order-Proportional-Integral-Derivative Controller
by Thanh Ha Nguyen, Tri Cuong Do, Van Du Phan and Kyoung Kwan Ahn
Mathematics 2023, 11(23), 4819; https://doi.org/10.3390/math11234819 - 29 Nov 2023
Cited by 4 | Viewed by 1586
Abstract
In recent years, reducing the energy consumption in a hydraulic excavator has received deep attention in many studies. The implementation of the novel independent metering valve system (NIMV) has emerged as a promising solution in this regard. However, external factors such as noise, [...] Read more.
In recent years, reducing the energy consumption in a hydraulic excavator has received deep attention in many studies. The implementation of the novel independent metering valve system (NIMV) has emerged as a promising solution in this regard. However, external factors such as noise, throttling loss, and leakage have negative influences on the tracking precision and energy saving in the NIMV system. In this paper, a novel control method, simple but effective, called a neural network-fractional order-proportional-integral-derivative controller is developed for the NIMV system. In detail, the fractional order-proportional-integral-derivative (FOPID) controller is used to improve the precision, stability, and fast response of the control system due to the inclusion of non-integer orders in the proportional, integral, and derivative terms. Along with that, the auto-tuning algorithm of the neural network controller is applied for adjusting five parameters in the FOPID controller under noise, throttling loss, and leakage. In addition, the proposed controller alleviates the amount of calculation for the system by using model-free control. To verify the effectiveness of the proposed controller, the simulation and experiment are conducted on the AMESim/MATLAB and a real test bench. As a result, the proposed controller not only operates the NIMV system accurately in the target trajectory but also reduces energy consumption, saving up 23.33% and 29.25% compared to FOPID and PID in the experimental platform, respectively. Full article
(This article belongs to the Special Issue Control, Modeling, Analysis, Optimization and Applications in Mechanical Engineering)
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22 pages, 12797 KiB  
Article
On the Application of Neural Networks Trained with FEM Data for the Identification of Stiffness Parameters of Improved Mechanical Beam Joints
by Francisco Badea, JesusAngel Perez, Fikret Can Ozenli and José Luis Olazagoitia
Mathematics 2023, 11(15), 3261; https://doi.org/10.3390/math11153261 - 25 Jul 2023
Cited by 1 | Viewed by 1468
Abstract
Even though beam-type elements are widely adopted in the industry due to their low computational cost and potential time savings when modeling, they present a significant shortcoming given by their own formulation, which makes them incapable of accounting for local joint topology, which [...] Read more.
Even though beam-type elements are widely adopted in the industry due to their low computational cost and potential time savings when modeling, they present a significant shortcoming given by their own formulation, which makes them incapable of accounting for local joint topology, which has a notable influence on the behavior of these structures. In this scenario, solutions that can mitigate this drawback while still providing improved results with simple models are of special interest. Many research works have focused on joint-specific approaches, as reflected in the literature. This paper introduces a novel generally improved beam model. This model uniquely features 4 nodes, 12 elastic elements, and 1 beam, contrasting starkly with the conventional beam elements that consist of merely 2 nodes and 1 element. This innovative model enhances the adaptability of modeled structures at the joint level. Crucially, it necessitates a methodology for the precise estimation of the elastic elements at the joint level. This article explores the capabilities of artificial neural networks for predicting the stiffness values derived from the calculated displacements at specific points within a complete structure. This research provides a complete analysis of the proposed methodology showing the significant limitations encountered for ANN when predicting finite element methodology (FEM)-derived values. The results and findings obtained in the article serve as a valuable reference paving the way for future studies involving finite element models and artificial neural networks. Full article
(This article belongs to the Special Issue Control, Modeling, Analysis, Optimization and Applications in Mechanical Engineering)
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