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Algorithms
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Dynamic System Modelling from Data: Emerging Algorithms and Applications: 2nd...
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Dynamic System Modelling from Data: Emerging Algorithms and Applications: 2nd Edition

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 1656

Special Issue Editors

Prof. Dr. Quanmin Zhu

E-Mail Website
Guest Editor
School of Engineering, University of the West of England-Frenchy Campus, Coldharbour Lane, Bristol, UK
Interests: dynamic system modeling; identification; control; simulation
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jing Chen

E-Mail Website
Guest Editor
School of Science, Jiangnan University, Wuxi 214126, China
Interests: processing control; system identification
Special Issues, Collections and Topics in MDPI journals
Dr. Ya Gu

E-Mail Website
Guest Editor
College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 201418, China
Interests: system identification; system modeling; artificial intelligence; deep learning; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Identification techniques for modelling from data, rather than from physical and chemical principles, usually include data processing, model structure detection, model parameter estimation, and post-validation. With fast-changing technology and ever-increasing computing capacity, many emerging algorithms in the fields of machine learning, big data, soft-sensor techniques, and reinforcement learning can realistically find applications in the identification of modern systems, ranging from manmade (engineering) to natural domains. On the other hand, no matter whatever algorithm is considered, some inherent issues must be overcome in one way or another, such as the proper handling of data uncertainty due to imperfect measurements that result in the presence of noise, time-delays, and data losses. Hence, one of the current challenges in this field is the development of identification algorithms that could yield compact mathematical models which would be useful for providing simple solutions to complex problems within a rigorous analytical framework.

The aim of this Special Issue is to report emerging novel identification algorithms for system modelling from data. The Editors welcome submissions in the form of regular technical reports, comprehensive surveys, and case studies.

Specific topics of interest include the following:

  • Novel identification algorithms for systems with time-delays.
  • Recent developments of machine learning algorithms and neural networks.
  • Modelling, analysis, and intelligent control of dynamic systems.
  • Algorithms with enhanced knowledge for intelligent automation.
  • Large-scale systems: structure detection/construction and parameter estimation.
  • Networked control system identification.
  • Neuro-fuzzy and other inductive algorithms in theory and/or applications.

Prof. Dr. Quanmin Zhu
Prof. Dr. Jing Chen
Dr. Ya Gu
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. Algorithms 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 1800 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

  • dynamic system modelling
  • data-driven identification
  • intelligent algorithms
  • applications

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Related Special Issue

Published Papers (2 papers)

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21 pages, 2419 KB  
Article
Application Features of a VOF Method for Simulating Boiling and Condensation Processes
by Andrey Kozelkov, Andrey Kurkin, Andrey Puzan, Vadim Kurulin, Natalya Tarasova and Vitaliy Gerasimov
Algorithms 2025, 18(10), 604; https://doi.org/10.3390/a18100604 - 26 Sep 2025
Viewed by 375
Abstract
This article presents the results of a study on the possibility of using a single-speed multiphase model with free surface allowance for simulating boiling and condensation processes. The simulation is based on the VOF method, which allows the position of the interphase boundary [...] Read more.
This article presents the results of a study on the possibility of using a single-speed multiphase model with free surface allowance for simulating boiling and condensation processes. The simulation is based on the VOF method, which allows the position of the interphase boundary to be tracked. To increase the stability of the iterative procedure for numerically solving volume fraction transfer equations using a finite volume discretization method on arbitrary unstructured grids, the basic VOF method is been modified by writing these equations in a semi-divergent form. The models of Tanasawa, Lee, and Rohsenow are considered models of interphase mass transfer, in which the evaporated or condensed mass linearly depends on the difference between the local temperature and the saturation temperature with accuracy in empirical parameters. This paper calibrates these empirical parameters for each mass transfer model. The results of our study of the influence of the values of the empirical parameters of models on the intensity of boiling and evaporation, as well as on the dynamics of the interphase boundary, are presented. This research is based on Stefan’s problem of the movement of the interphase boundary due to the evaporation of a liquid and the problem of condensation of vapor bubbles water columns. As a result of a series of numerical experiments, it is shown that the average error in the position of the interfacial boundary for the Tanasawa and Lee models does not exceed 3–6%. For the Rohsenow model, the result is somewhat worse, since the interfacial boundary moves faster than it should move according to calculations based on analytical formulas. To investigate the possibility of condensation modeling, the results of a numerical solution of the problem of an emerging condensing vapor bubble are considered. A numerical assessment of its position in space and the shape and dynamics of changes in its diameter over time is carried out using the VOF method, taking into account the free surface. It is shown herein that the Tanasawa model has the highest accuracy for modeling the condensation process using a VOF method taking into account the free surface, while the Rohsenow model is most unstable and prone to deformation of the bubble shape. At the same time, the dynamics of bubble ascent are modeled by all three models. The results obtained confirm the fundamental possibility of using a VOF method to simulate the processes of boiling and condensation and taking into account the dynamics of the free surface. At the same time, the problem of the studied models of phase transitions is revealed, which consists of the need for individual selection of optimal values of empirical parameters for each specific task. Full article
(This article belongs to the Special Issue Dynamic System Modelling from Data: Emerging Algorithms and Applications: 2nd Edition)
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22 pages, 2789 KB  
Article
Longitudinal Tire Force Estimation Method for 4WIDEV Based on Data-Driven Modified Recursive Subspace Identification Algorithm
by Xiaoyu Wang, Te Chen and Jiankang Lu
Algorithms 2025, 18(7), 409; https://doi.org/10.3390/a18070409 - 3 Jul 2025
Cited by 2 | Viewed by 537
Abstract
For the longitudinal tire force estimation problem of four-wheel independent drive electric vehicles (4WIDEVs), traditional model-based observers have limitations such as high modeling complexity and strong parameter sensitivity, while pure data-driven methods are susceptible to noise interference and have insufficient generalization ability. Therefore, [...] Read more.
For the longitudinal tire force estimation problem of four-wheel independent drive electric vehicles (4WIDEVs), traditional model-based observers have limitations such as high modeling complexity and strong parameter sensitivity, while pure data-driven methods are susceptible to noise interference and have insufficient generalization ability. Therefore, this study proposes a joint estimation framework that integrates data-driven and modified recursive subspace identification algorithms. Firstly, based on the electromechanical coupling mechanism, an electric drive wheel dynamics model (EDWM) is constructed, and multidimensional driving data is collected through a chassis dynamometer experimental platform. Secondly, an improved proportional integral observer (PIO) is designed to decouple the longitudinal force from the system input into a state variable, and a subspace identification recursive algorithm based on correction term with forgetting factor (CFF-SIR) is introduced to suppress the residual influence of historical data and enhance the ability to track time-varying parameters. The simulation and experimental results show that under complex working conditions without noise and interference, with noise influence (5% white noise), and with interference (5% irregular signal), the mean and mean square error of longitudinal force estimation under the CFF-SIR algorithm are significantly reduced compared to the correction-based subspace identification recursive (C-SIR) algorithm, and the comprehensive estimation accuracy is improved by 8.37%. It can provide a high-precision and highly adaptive longitudinal force estimation solution for vehicle dynamics control and intelligent driving systems. Full article
(This article belongs to the Special Issue Dynamic System Modelling from Data: Emerging Algorithms and Applications: 2nd Edition)
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