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Axioms
Special Issues
Control Theory and Control Systems: Algorithms and Methods
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Special Issue "Control Theory and Control Systems: Algorithms and Methods"

A special issue of Axioms (ISSN 2075-1680). This special issue belongs to the section "Mathematical Physics".

Deadline for manuscript submissions: 20 January 2024 | Viewed by 1753

Special Issue Editors

Dr. Artem Bykov

E-Mail Website
Guest Editor
1. Department of Data Analysis and Machine Learning, Financial University under the Government of the Russian Federation, Leningrad Avenue 49, 125993 Moscow, Russia
2. Department of Software Engineering, Murom Institute (branch) of Vladimir State University, St. Orlovskaya 23, 602264 Murom, Russia
Interests: geophysics; geodynamic control; data processing; electro-seismic methods of soil sounding; mathematical modeling
Dr. Svetlana Polukoshko

E-Mail Website
Co-Guest Editor
Scientific Research Institute “VSRC”, Ventspils University of Applied Sciences, Inženieru 101, LV3601 Ventspils, Latvia
Interests: mathematical modeling; nonlinear dynamics; vibroimpact systems; vibration of machines and constructions elements and their vibroinsulation; material sciences; earth sciences

Special Issue Information

Dear Colleagues,

We are accepting articles for a Special Issue of the journal Axiomsс (ISSN 2075-1680, https://www.mdpi.com/journal/axioms), titled "Control Theory and Control Systems: Algorithms and Methods". The subject of the journal is a collection of current research in the field of data processing obtained in monitoring systems for various media, a collection of modern technologies for modeling an experiment, and the results of large-scale engineering and geological experiments. The purpose of the journal is to provide a high-level platform for scientists and experts from all over the world to exchange and discuss the latest ideas and results within theoretical and practical research.

Dr. Artem Bykov
Dr. Svetlana Polukoshko
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. Axioms 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

  • machine learning algorithms
  • data-processing algorithms
  • control systems
  • control theory
  • optimal control
  • dynamic objects
  • transmission of information

Published Papers (2 papers)

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Article
Global Fixed-Time Sliding Mode Trajectory Tracking Control Design for the Saturated Uncertain Rigid Manipulator
by
Jun Nie
,
Lichao Hao
,
Xiao Lu
,
Haixia Wang
and
Chunyang Sheng
Axioms 2023, 12(9), 883; https://doi.org/10.3390/axioms12090883 - 15 Sep 2023
Viewed by 203
Abstract
The global fixed-time sliding mode control strategy is designed for the manipulator to achieve global fixed-time trajectory tracking in response to the uncertainty of the system model, the external disturbances, and the saturation of the manipulator actuator. First, aiming at the lumped disturbance [...] Read more.
The global fixed-time sliding mode control strategy is designed for the manipulator to achieve global fixed-time trajectory tracking in response to the uncertainty of the system model, the external disturbances, and the saturation of the manipulator actuator. First, aiming at the lumped disturbance caused by system model uncertainty and external disturbance, the adaptive fixed-time sliding mode disturbance observer (AFSMDO) was introduced to eliminate the negative effects of disturbance. The observer parameters can adaptively change with disturbances by designing the adaptive law, improving the accuracy of disturbance estimation. Secondly, the fixed-time sliding surface was introduced to avoid singularity, and the nonsingular fixed-time sliding mode control (NFSMC) design was put in place to ensure the global convergence of the manipulator system. Finally, the fixed time saturation compensator (FTSC) was created for NFSMC to prevent the negative impact of actuator saturation on the manipulator system, effectively reducing system chatter and improving the response speed of the closed-loop system. The fixed-time stability theory and Lyapunov method were exploited to offer a thorough and rigorous theoretical analysis and stability demonstration for the overall control system. Simulation experiments verify that the designed control scheme has excellent control effects and strong practicability. Full article
(This article belongs to the Special Issue Control Theory and Control Systems: Algorithms and Methods)
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Article
Fault Detection and Identification with Kernel Principal Component Analysis and Long Short-Term Memory Artificial Neural Network Combined Method
by
Nahid Jafari
and
António M. Lopes
Axioms 2023, 12(6), 583; https://doi.org/10.3390/axioms12060583 - 12 Jun 2023
Viewed by 547
Abstract
A new fault detection and identification approach is proposed. The kernel principal component analysis (KPCA) is first applied to the data for reducing dimensionality, and the occurrence of faults is determined by means of two statistical indices, T2 and Q. The [...] Read more.
A new fault detection and identification approach is proposed. The kernel principal component analysis (KPCA) is first applied to the data for reducing dimensionality, and the occurrence of faults is determined by means of two statistical indices, T2 and Q. The K-means clustering algorithm is then adopted to analyze the data and perform clustering, according to the type of fault. Finally, the type of fault is determined using a long short-term memory (LSTM) neural network. The performance of the proposed technique is compared with the principal component analysis (PCA) method in early detecting malfunctions on a continuous stirred tank reactor (CSTR) system. Up to 10 sensor faults and other system degradation conditions are considered. The performance of the LSTM neural network is compared with three other machine learning techniques, namely the support vector machine (SVM), K-nearest neighbors (KNN) algorithm, and decision trees, in determining the type of fault. The results indicate the superior performance of the suggested methodology in both early fault detection and fault identification. Full article
(This article belongs to the Special Issue Control Theory and Control Systems: Algorithms and Methods)
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