Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
2.0
Journals
Axioms
Special Issues
Control Theory and Control Systems: Algorithms and Methods
share announcement

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: closed (20 January 2024) | Viewed by 5927

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 (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 5987 KiB  
Article
Optimizing Controls to Track Moving Targets in an Intelligent Electro-Optical Detection System
by Cheng Shen, Zhijie Wen, Wenliang Zhu, Dapeng Fan and Mingyuan Ling
Axioms 2024, 13(2), 113; https://doi.org/10.3390/axioms13020113 - 08 Feb 2024
Viewed by 837
Abstract
Electro-optical detection systems face numerous challenges due to the complexity and difficulty of targeting controls for “low, slow and tiny” moving targets. In this paper, we present an optimal model of an advanced n-step adaptive Kalman filter and gyroscope short-term integration weighting fusion [...] Read more.
Electro-optical detection systems face numerous challenges due to the complexity and difficulty of targeting controls for “low, slow and tiny” moving targets. In this paper, we present an optimal model of an advanced n-step adaptive Kalman filter and gyroscope short-term integration weighting fusion (nKF-Gyro) method with targeting control. A method is put forward to improve the model by adding a spherical coordinate system to design an adaptive Kalman filter to estimate target movements. The targeting error formation is analyzed in detail to reveal the relationship between tracking controller feedback and line-of-sight position correction. Based on the establishment of a targeting control coordinate system for tracking moving targets, a dual closed-loop composite optimization control model is proposed. The outer loop is used for estimating the motion parameters and predicting the future encounter point, while the inner loop is used for compensating the targeting error of various elements in the firing trajectory. Finally, the modeling method is substituted into the disturbance simulation verification, which can monitor and compensate for the targeting error of moving targets in real time. The results show that in the optimal model incorporating the nKF-Gyro method with targeting control, the error suppression was increased by up to 36.8% compared to that of traditional KF method and was 25% better than that of the traditional nKF method. Full article
(This article belongs to the Special Issue Control Theory and Control Systems: Algorithms and Methods)
►▼ Show Figures

Figure 1

24 pages, 652 KiB  
Article
Finite-Time Passivity and Synchronization for a Class of Fuzzy Inertial Complex-Valued Neural Networks with Time-Varying Delays
by Jing Han
Axioms 2024, 13(1), 39; https://doi.org/10.3390/axioms13010039 - 07 Jan 2024
Cited by 1 | Viewed by 770
Abstract
This article investigates finite-time passivity for fuzzy inertial complex-valued neural networks (FICVNNs) with time-varying delays. First, by using the existing passivity theory, several related definitions of finite-time passivity are illustrated. Consequently, by adopting a reduced-order method and dividing complex-valued parameters into real and [...] Read more.
This article investigates finite-time passivity for fuzzy inertial complex-valued neural networks (FICVNNs) with time-varying delays. First, by using the existing passivity theory, several related definitions of finite-time passivity are illustrated. Consequently, by adopting a reduced-order method and dividing complex-valued parameters into real and imaginary parts, the proposed FICVNNs are turned into first-order real-valued neural network systems. Moreover, appropriate controllers and the Lyapunov functional method are established to obtain the finite-time passivity of FICVNNs with time delays. Furthermore, some essential conditions are established to ensure finite-time synchronization for finite-time passive FICVNNs. In the end, corresponding simulations certify the feasibility of the proposed theoretical outcomes. Full article
(This article belongs to the Special Issue Control Theory and Control Systems: Algorithms and Methods)
►▼ Show Figures

Figure 1

15 pages, 801 KiB  
Article
Partial Singular Value Assignment for Large-Scale Systems
by Yiting Huang, Qiong Tang and Bo Yu
Axioms 2023, 12(11), 1012; https://doi.org/10.3390/axioms12111012 - 27 Oct 2023
Viewed by 651
Abstract
The partial singular value assignment problem stems from the development of observers for discrete-time descriptor systems and the resolution of ordinary differential equations. Conventional techniques mostly utilize singular value decomposition, which is unfeasible for large-scale systems owing to their relatively high complexity. By [...] Read more.
The partial singular value assignment problem stems from the development of observers for discrete-time descriptor systems and the resolution of ordinary differential equations. Conventional techniques mostly utilize singular value decomposition, which is unfeasible for large-scale systems owing to their relatively high complexity. By calculating the sparse basis of the null space associated with some orthogonal projections, the existence of the matrix in partial singular value assignment is proven and an algorithm is subsequently proposed for implementation, effectively avoiding the full singular value decomposition of the existing methods. Numerical examples exhibit the efficiency of the presented method. Full article
(This article belongs to the Special Issue Control Theory and Control Systems: Algorithms and Methods)
►▼ Show Figures

Figure 1

26 pages, 17935 KiB  
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
Cited by 1 | Viewed by 793
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)
►▼ Show Figures

Figure 1

15 pages, 1777 KiB  
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 1253
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop