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Machines
Special Issues
Cutting-Edge Technologies and Applications in Automatic Control Systems
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Cutting-Edge Technologies and Applications in Automatic Control Systems

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Automation and Control Systems".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 2629

Special Issue Editor

Dr. Jan Gorecki

E-Mail Website
Guest Editor
Faculty of Mechanical Engineering, Institute of Machine Design, Poznan University of Technology, 60-965 Poznań, Poland
Interests: applied mechanics; machine design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the latest advancements and practical applications of state-of-the-art technologies in the field of automatic control systems. The continuous evolution of technology has paved the way for innovative approaches and methodologies, revolutionizing the design and implementation of control systems across various industries.

The objective of this Special Issue is to provide a platform for researchers, engineers, and industry experts to exchange their knowledge, experiences, and insights on the cutting-edge technologies that are shaping the future of automatic control systems. This publication will serve as a valuable resource for both academia and industry professionals seeking to stay updated with the latest developments and leverage these technologies to enhance system performance, efficiency, and reliability.

We welcome contributions in the following areas:

  1. Artificial intelligence and machine learning in control systems
  2. Adaptive and self-learning control systems
  3. Robotics and autonomous control
  4. Industrial automation and process optimization
  5. Intelligent transportation systems and autonomous vehicles

Dr. Jan Gorecki
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. Machines is an international peer-reviewed open access monthly 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 2400 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

  • automatic control systems
  • advanced control strategies
  • machine learning
  • intelligent automation
  • machine design

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

Published Papers (2 papers)

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Research

36 pages, 42325 KiB  
Article
Validation of Ecology and Energy Parameters of Diesel Exhausts Using Different Fuel Mixtures, Consisting of Hydrogenated Vegetable Oil and Diesel Fuels, Presented at Real Market: Approaches Using Artificial Neural Network for Large-Scale Predictions
by Jonas Matijošius, Alfredas Rimkus and Alytis Gruodis
Machines 2024, 12(6), 353; https://doi.org/10.3390/machines12060353 - 21 May 2024
Viewed by 624
Abstract
Machine learning models have been used to precisely forecast emissions from diesel engines, specifically examining the impact of various fuel types (HVO10, HVO 30, HVO40, HVO50) on the accuracy of emission forecasts. The research has revealed that models with different numbers of perceptrons [...] Read more.
Machine learning models have been used to precisely forecast emissions from diesel engines, specifically examining the impact of various fuel types (HVO10, HVO 30, HVO40, HVO50) on the accuracy of emission forecasts. The research has revealed that models with different numbers of perceptrons had greater initial error rates, which subsequently reached a stable state after further training. Additionally, the research has revealed that augmenting the proportion of Hydrogenated Vegetable Oil (HVO) resulted in the enhanced precision of emission predictions. The use of visual data representations, such as histograms and scatter plots, yielded significant insights into the model’s versatility across different fuel types. The discovery of these results is vital for enhancing engine performance and fulfilling environmental regulations. This study highlights the capacity of machine learning in monitoring the environment and controlling engines and proposes further investigation into enhancing models and making real-time predictive adjustments. The novelty of the research is based on the determination of the input interface (a sufficient amount of input parameters, including chemical as well as technical), which characterizes the different regimes of the diesel engine. The novelty of the methodology is based on the selection of a suitable ANN type and architecture, which allows us to predict the required parameters for a wide range of input intervals (different types of mixtures consisting of HVO and pure diesel, different loads, different RPMs, etc.). Full article
(This article belongs to the Special Issue Cutting-Edge Technologies and Applications in Automatic Control Systems)
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24 pages, 40484 KiB  
Article
Validation Challenges in Data for Different Diesel Engine Performance Regimes Utilising HVO Fuel: A Study on the Application of Artificial Neural Networks for Emissions Prediction
by Jonas Matijošius, Alfredas Rimkus and Alytis Gruodis
Machines 2024, 12(4), 279; https://doi.org/10.3390/machines12040279 - 21 Apr 2024
Cited by 1 | Viewed by 1457
Abstract
Artificial neural networks (ANNs) provide supervised learning via input pattern assessment and effective resource management, thereby improving energy efficiency and predicting environmental fluctuations. The advanced technique of ANNs forecasts diesel engine emissions by collecting measurements during trial sessions. This study included experimental sessions [...] Read more.
Artificial neural networks (ANNs) provide supervised learning via input pattern assessment and effective resource management, thereby improving energy efficiency and predicting environmental fluctuations. The advanced technique of ANNs forecasts diesel engine emissions by collecting measurements during trial sessions. This study included experimental sessions to establish technical and ecological indicators for a diesel engine across several operational scenarios. VALLUM01, a novel tool, has been created with a user-friendly interface for data input/output, intended for the purposes of testing and prediction. There was a comprehensive collection of 12 input parameters and 10 output parameters that were identified as relevant and sufficient for the objectives of training, validation, and prediction. The proper value ranges for transforming into fuzzy sets for input/output to an ANN were found. Given that the ANN’s training session comprises 1,000,000 epochs and 1000 perceptrons within a single-hidden layer, its effectiveness can be considered high. Many statistical distributions, including Pearson, Spearman, and Kendall, validate the prediction accuracy. The accuracy ranges from 96% on average, and in some instances, it may go up to 99%. Full article
(This article belongs to the Special Issue Cutting-Edge Technologies and Applications in Automatic Control Systems)
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