Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.5
2.4
Journals
Mathematics
Special Issues
Advance in Control Theory and Optimization
share announcement

Advance in Control Theory and Optimization

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 1503

Special Issue Editors

Prof. Dr. Zhi Li

E-Mail Website
Guest Editor
School of Mechano-Electronic Engineering, Xidian University, Shaanxi, Xi’an 710000, China
Interests: complex multi-intelligent network; collaborative control theory and application; group intelligent decision-making and optimization; collaborative control of multiple drones, unmanned vehicles, and robots
Dr. Sihai Guan

E-Mail Website
Guest Editor
College of Electronic and Information, Southwest Minzu University, Chengdu, 610041, China
Interests: adaptive signal processing; blood oxygen level dependent (BOLD) signal analysis; machine learning; entropy and fractal analysis

Special Issue Information

Dear Colleagues,

This Special Issue, titled “Advance in Control Theory and Optimization”, is dedicated to researchers specializing in mathematical methods within the fields of control theory and optimization. The primary objective of this Special Issue is to collect the latest advancements in mathematical methods and algorithms within the fields of control theory and optimization, and provide significant theoretical support and practical methodologies for addressing complex systems and practical problems. This Special Issue is dedicated to a wide range of scientific subjects, including complex modeling systems, artificial intelligence, optimization and scheduling, the analysis of control systems, and collaborative control theory.

Our scope covers a broad array of topics, including, but not limited to:

  • The modeling, control, and optimization of complex systems;
  • The control of stochastic systems;
  • Adaptive control and learning control;
  • Neural networks and deep learning;
  • Game evolution and intelligent decision making;
  • Optimization and scheduling;
  • Multi-agent collaborative control and optimization;
  • Discrete event dynamic systems;
  • Constrained control.

We look forward to receiving your contributions and the wealth of knowledge you will bring to this Special Issue. Let us continue to push the advances in control theory and optimization.

Prof. Dr. Zhi Li
Dr. Sihai Guan
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

  • complex systems
  • control and optimization
  • stochastic systems
  • adaptive control
  • learning control
  • discrete event systems
  • multi-agent systems

Published Papers (3 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

18 pages, 2563 KiB  
Article
Neural Network-Based Distributed Consensus Tracking Control for Nonlinear Multi-Agent Systems with Mismatched and Matched Disturbances
by Linxi Xu and Kaiyu Qin
Mathematics 2024, 12(9), 1319; https://doi.org/10.3390/math12091319 - 26 Apr 2024
Viewed by 303
Abstract
In practice, disturbances, including model uncertainties and unknown external disturbances, are always widely present and have a significant impact on the cooperative control performance of a networked multi-agent system. In this work, the distributed consensus tracking control problem for a class of multi-agent [...] Read more.
In practice, disturbances, including model uncertainties and unknown external disturbances, are always widely present and have a significant impact on the cooperative control performance of a networked multi-agent system. In this work, the distributed consensus tracking control problem for a class of multi-agent systems subject to matched and mismatched uncertainties is addressed. In particular, the dynamics of the leader agent are modeled with uncertain terms, i.e., the leader’s higher-order information, such as velocity and acceleration, is unknown to all followers. To solve this problem, a robust consensus tracking control scheme that combines a neural network-based distributed observer, a barrier function-based disturbance observer, and a tracking controller based on the back-stepping method was developed in this study. Firstly, a neural network-based distributed observer is designed, which is able to achieve effective estimation of leader information by all followers. Secondly, a tracking controller was designed utilizing the back-stepping technique, and the boundedness of the closed-loop error system was proved using the Lyapunov-like theorem, which enables the followers to effectively track the leader’s trajectory. Meanwhile, a barrier function-based disturbance observer is proposed, which achieves the effective estimation of matched and mismatched uncertainties of followers. Finally, the effectiveness of the robust consensus tracking control method designed in this study was verified through numerical simulations. Full article
(This article belongs to the Special Issue Advance in Control Theory and Optimization)
►▼ Show Figures

Figure 1

16 pages, 1832 KiB  
Article
Multi-Objective Optimization of Cell Voltage Based on a Comprehensive Index Evaluation Model in the Aluminum Electrolysis Process
by Chenhua Xu, Wenjie Zhang, Dan Liu, Jian Cen, Jianbin Xiong and Guojuan Luo
Mathematics 2024, 12(8), 1174; https://doi.org/10.3390/math12081174 - 14 Apr 2024
Viewed by 477
Abstract
In the abnormal situation of an aluminum electrolysis cell, the setting of cell voltage is mainly based on manual experience. To obtain a smaller cell voltage and optimize the operating parameters, a multi-objective optimization method for cell voltage based on a comprehensive index [...] Read more.
In the abnormal situation of an aluminum electrolysis cell, the setting of cell voltage is mainly based on manual experience. To obtain a smaller cell voltage and optimize the operating parameters, a multi-objective optimization method for cell voltage based on a comprehensive index evaluation model is proposed. Firstly, a comprehensive judgment model of the cell state based on the energy balance, material balance, and stability of the aluminum electrolysis process is established. Secondly, a fuzzy neural network (FNN) based on the autoregressive moving average (ARMA) model is designed to establish the cell-state prediction model in order to finish the real-time monitoring of the process. Thirdly, the optimization goal of the process is summarized as having been met when the difference between the average cell voltage and the target value reaches the minimum, and the condition of the cell is excellent. And then, the optimization setting model of cell voltage is established under the constraints of the production and operation requirements. Finally, a multi-objective antlion optimization algorithm (MOALO) is used to solve the above model and find a group of optimized values of the electrolysis cell, which is used to realize the optimization control of the cell state. By using actual production data, the above method is validated to be effective. Moreover, optimized operating parameters are used to verify the prediction model of cell voltage, and the cell state is just excellent. The method is also applied to realize the optimization control of the process. It is of guiding significance for stabilizing the electrolytic aluminum production and achieving energy saving and consumption reduction. Full article
(This article belongs to the Special Issue Advance in Control Theory and Optimization)
►▼ Show Figures

Figure 1

29 pages, 6845 KiB  
Article
Research on Improved Differential Evolution Particle Swarm Hybrid Optimization Method and Its Application in Camera Calibration
by Xinyu Sha, Fucai Qian and Hongli He
Mathematics 2024, 12(6), 870; https://doi.org/10.3390/math12060870 - 15 Mar 2024
Viewed by 481
Abstract
The calibration of cameras plays a critical role in close-range photogrammetry because the precision of calibration has a direct effect on the quality of results. When handling image capture using a camera, traditional swarm intelligence algorithms such as genetic algorithms and particle swarm [...] Read more.
The calibration of cameras plays a critical role in close-range photogrammetry because the precision of calibration has a direct effect on the quality of results. When handling image capture using a camera, traditional swarm intelligence algorithms such as genetic algorithms and particle swarm optimization, in conjunction with Zhang’s calibration method, frequently face difficulties regarding local optima and sluggish convergence. This study presents an enhanced hybrid optimization approach utilizing both the principles of differential evolution and particle swarm optimization, which is then employed in the context of camera calibration. Initially, we establish a measurement model specific to the camera in close-range photogrammetry and determine its interior orientation parameters. Subsequently, employing these parameters as initial values, we perform global optimization and iteration using the improved hybrid optimization algorithm. The effectiveness of the proposed approach is subsequently validated through simulation and comparative experiments. Compared to alternative approaches, the proposed algorithm enhances both the accuracy of camera calibration and the convergence speed. It effectively addresses the issue of other algorithms getting trapped in local optima due to image distortion. These research findings provide theoretical support for practical engineering applications in the field of control theory and optimization to a certain extent. Full article
(This article belongs to the Special Issue Advance in Control Theory and Optimization)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop