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Mathematics
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Identification, Knowledge Engineering and Digital Modeling for Adaptive and Intelligent...
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Identification, Knowledge Engineering and Digital Modeling for Adaptive and Intelligent Control, 2nd Edition

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 4568

Special Issue Editors

Prof. Dr. Natalia Bakhtadze

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Guest Editor
Institute for Control Sciences, Russian Academy of Sciences, 117806 Moscow, Russia
Interests: identification of control systems; estimation theory; adaptive control; model predictive control; data mining; wavelet analysis; control of technological processes in industry and energy; multi-agent systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Igor Yadykin

E-Mail Website
Guest Editor
V.A. Trapeznikov Institute of Control Sciences, 65, Profsoyuznaya, 117997 Moscow, Russia
Interests: power systems analysis; power systems simulation; adaptive and optimal control; mechanical engineering
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Andrei Torgashov

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Guest Editor
Institute of Automation and Control Process FEB RAS, 5 Radio Str., 690041 Vladivostok, Russia
Interests: system identification; predictive modeling; advanced process control
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Nikolay Korgin

E-Mail Website
Guest Editor
V.A. Trapeznikov Institute of Control Sciences, 65, Profsoyuznaya, 117997 Moscow, Russia
Interests: mechanism design; game theory; power systems analysis; mechanical engineering; identification problems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advent of digitalization at the end of the 20th century drastically changed current approaches to management and control. Businesses which apply data-driven strategies and knowledge engineering have a competitive advantage. Intelligent control techniques based on neural networks, fuzzy models, and machine and reinforcement learning demonstrate the highest performance. Digital twins are becoming increasingly popular.

The aim of this Special Issue is to review and discuss the recent advances and novelties in the field of intelligent control with adjustable models.

Researchers in these fields are invited to discuss control problems such as: the creation of enterprise control and digital ecosystems; identification theory, methodology and the related mathematical problems; parametric, nonparametric and structural identification; control systems with an identifier; modelling in intelligent systems; simulation procedures and software; digital identification; reinforcement learning; quantum modeling; intelligence in model predictive control; predictive cognitive methods; software quality for complex systems; and global network resources for modeling and control.

We welcome both research and overview articles and look forward to an active discussion on these and related issues.

Prof. Dr. Natalia Bakhtadze
Prof. Dr. Igor Yadykin
Prof. Dr. Andrei Torgashov
Prof. Dr. Nikolay Korgin
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

  • identification
  • intelligent model
  • predictive control enterprise control
  • digital ecosystem creating
  • reinforcement learning
  • quantum modeling
  • situational awareness 
  • digital twins

Published Papers (5 papers)

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Research

13 pages, 487 KiB  
Article
Volterra Black-Box Models Identification Methods: Direct Collocation vs. Least Squares
by Denis Sidorov, Aleksandr Tynda, Vladislav Muratov and Eugeny Yanitsky
Mathematics 2024, 12(2), 227; https://0-doi-org.brum.beds.ac.uk/10.3390/math12020227 - 10 Jan 2024
Viewed by 544
Abstract
The Volterra integral-functional series is the classic approach for nonlinear black box dynamical system modeling. It is widely employed in many domains including radiophysics, aerodynamics, electronic and electrical engineering and many others. Identifying the time-varying functional parameters, also known as Volterra kernels, poses [...] Read more.
The Volterra integral-functional series is the classic approach for nonlinear black box dynamical system modeling. It is widely employed in many domains including radiophysics, aerodynamics, electronic and electrical engineering and many others. Identifying the time-varying functional parameters, also known as Volterra kernels, poses a difficulty due to the curse of dimensionality. This refers to the exponential growth in the number of model parameters as the complexity of the input-output response increases. The least squares method (LSM) is widely acknowledged as the standard approach for tackling the issue of identifying parameters. Unfortunately, the LSM suffers with many drawbacks such as the sensitivity to outliers causing biased estimation, multicollinearity, overfitting and inefficiency with large datasets. This paper presents an alternative approach based on direct estimation of the Volterra kernels using the collocation method. Two model examples are studied. It is found that the collocation method presents a promising alternative for optimization, surpassing the traditional least squares method when it comes to the Volterra kernels identification including the case when input and output signals suffer from considerable measurement errors. Full article
(This article belongs to the Special Issue Identification, Knowledge Engineering and Digital Modeling for Adaptive and Intelligent Control, 2nd Edition)
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16 pages, 2381 KiB  
Article
Trust in Artificial Intelligence: Modeling the Decision Making of Human Operators in Highly Dangerous Situations
by Alexander L. Venger and Victor M. Dozortsev
Mathematics 2023, 11(24), 4956; https://0-doi-org.brum.beds.ac.uk/10.3390/math11244956 - 14 Dec 2023
Viewed by 1288
Abstract
A prescriptive simulation model of a process operator’s decision making assisted with an artificial intelligence (AI) algorithm in a technical system control loop is proposed. Situations fraught with a catastrophic threat that may cause unacceptable damage were analyzed. The operators’ decision making was [...] Read more.
A prescriptive simulation model of a process operator’s decision making assisted with an artificial intelligence (AI) algorithm in a technical system control loop is proposed. Situations fraught with a catastrophic threat that may cause unacceptable damage were analyzed. The operators’ decision making was interpreted in terms of a subjectively admissible probability of disaster and subjectively necessary reliability of its assessment, which reflect the individual psychological aspect of operator’s trust in AI. Four extreme decision-making strategies corresponding to different ratios between the above variables were distinguished. An experiment simulating a process facility, an AI algorithm and operator’s decision making strategy was held. It showed that depending on the properties of a controlled process (its dynamics and the hazard onset’s speed) and the AI algorithm characteristics (Type I and II error rate), each of such strategies or some intermediate strategy may prove to be more beneficial than others. The same approach is applicable to the identification and analysis of sustainability of strategies applied in real-life operating conditions, as well as to the development of a computer simulator to train operators to control hazardous technological processes using AI-generated advice. Full article
(This article belongs to the Special Issue Identification, Knowledge Engineering and Digital Modeling for Adaptive and Intelligent Control, 2nd Edition)
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11 pages, 343 KiB  
Article
Integral Models in the Form of Volterra Polynomials and Continued Fractions in the Problem of Identifying Input Signals
by Svetlana Solodusha, Yuliya Kokonova and Oksana Dudareva
Mathematics 2023, 11(23), 4724; https://0-doi-org.brum.beds.ac.uk/10.3390/math11234724 - 22 Nov 2023
Viewed by 607
Abstract
The paper discusses the prospect of using a combined model based on finite segments (polynomials) of the Volterra integral power series. We consider a case when the problem of identifying the Volterra kernels is solved. The predictive properties of the classic Volterra polynomial [...] Read more.
The paper discusses the prospect of using a combined model based on finite segments (polynomials) of the Volterra integral power series. We consider a case when the problem of identifying the Volterra kernels is solved. The predictive properties of the classic Volterra polynomial are improved by adding a linear part in the form of an equivalent continued fraction. This technique allows us to distinguish an additional parameter—the connection coefficient α, which is effective in adapting the constructed integral model to changes in technical parameters at the input of a dynamic system. In addition, this technique allows us to take into account the case of perturbing the kernel of the linear term of the Volterra polynomial in the metric C[0,T] by a given value δ, implying the ideas of Volterra regularizing procedures. The problem of choosing the connection coefficient is solved using a special extremal problem. The developed algorithms are used to solve the problem of identifying input signals of test dynamic systems, among which, in addition to mathematical ones, thermal power engineering devices are used. Full article
(This article belongs to the Special Issue Identification, Knowledge Engineering and Digital Modeling for Adaptive and Intelligent Control, 2nd Edition)
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24 pages, 6407 KiB  
Article
Self-Tuning Controller Using Shifting Method
by Milan Hofreiter, Michal Moučka and Pavel Trnka
Mathematics 2023, 11(21), 4548; https://0-doi-org.brum.beds.ac.uk/10.3390/math11214548 - 04 Nov 2023
Viewed by 788
Abstract
This paper presents a newly implemented self-tuning PID controller that uses a relay feedback identification using a recently designed relay shifting method to determine the mathematical model of the process and subsequently adjust the controller parameters. The controller is applicable to proportional and [...] Read more.
This paper presents a newly implemented self-tuning PID controller that uses a relay feedback identification using a recently designed relay shifting method to determine the mathematical model of the process and subsequently adjust the controller parameters. The controller is applicable to proportional and integrating systems and is even applicable to systems with transport delays if steady-state oscillation can be achieved in the relay control of the system. After briefly introducing the relay shifting method, the current paper describes the hardware (HW) and software (SW) of the proposed controller in detail. The relay feedback identification and control of a laboratory setup by an automatically tuned controller is demonstrated on a real laboratory device called “Hot air tunnel”. The evaluation of the experiment and the characteristics of the controller are presented at the end of the paper. The advantage of the relay method is that it is not as computationally intensive as other identification methods. It can thus be implemented on more energy-efficient microcontrollers, which is very important nowadays. Full article
(This article belongs to the Special Issue Identification, Knowledge Engineering and Digital Modeling for Adaptive and Intelligent Control, 2nd Edition)
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20 pages, 1292 KiB  
Article
Identification of Inhomogeneities: The Selected Coordinate Descent Method Applied in the Drilling Area
by Tatyana Smaglichenko and Alexander Smaglichenko
Mathematics 2023, 11(20), 4297; https://0-doi-org.brum.beds.ac.uk/10.3390/math11204297 - 15 Oct 2023
Viewed by 789
Abstract
The exploration of inhomogeneities is a crucial factor for industries because of the necessary control of the quality of output products or the check adequacy of the data from the helping information systems. In the energy-conception field, the preliminary study of borehole areas [...] Read more.
The exploration of inhomogeneities is a crucial factor for industries because of the necessary control of the quality of output products or the check adequacy of the data from the helping information systems. In the energy-conception field, the preliminary study of borehole areas has special importance because it can avoid risks of secure drilling and financial expenses. In this paper, an innovative option of the traditional coordinate descent method called selected coordinate descent, was investigated by collating its fundamentals with other methods used in various industrial branches. A practical application of selected coordinate descent was performed for experimental data of seismic event registration observed in the region of geothermal plants. An explicit formula for the resolution parameter was utilized to distinguish well and poorly resolved anomalies. The inhomogeneities were validated on the basis of a good resolution and comparison with data from other disciplines. The main result of our study is the performance of the algebraic technique application in the reconstruction of large-size structures. The identification of the found seismic inhomogeneities permits us to indicate the sites that are questionable for drilling and to obtain knowledge about the rock types at crucial depths. Full article
(This article belongs to the Special Issue Identification, Knowledge Engineering and Digital Modeling for Adaptive and Intelligent Control, 2nd Edition)
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