Symmetry in Control System Theory and Applications

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Engineering and Materials".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 1025

Special Issue Editors


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Guest Editor
Associate Professor, Faculty of Electronic Engineering, Department of Control Systems, University of Niš, 18000 Niš, Serbia
Interests: AI; neural networks; machine learning; big data; SMC; orthogonal polynomials/filters; autonomous driving technology; cyber-physical systems

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Guest Editor
Associate Professor, Faculty of Electronic Engineering, Department of Control Systems, University of Niš, 18000 Niš, Serbia
Interests: sliding mode control; fuzzy systems; orthogonal polynomials and filters; genetic algorithms; neural networks

Special Issue Information

Dear Colleagues,

The purpose of this Special Issue is to gather articles pertaining to control system engineering, where design and analysis might benefit from symmetry. In general, symmetry serves as a powerful tool in control systems, offering a structured approach to understanding and designing control systems for a wide range of applications. Symmetry often implies a form of balance or equivalence, and in control theory, this can be leveraged to simplify system analysis and design. For instance, the symmetrical properties of a system can lead to structured control approaches that exploit this symmetry to reduce complexity and improve the performance. Additionally, symmetry can help in identifying invariant properties of a system under certain transformations, providing insights into its behavior and enabling more efficient control strategies. The concept of symmetry can also play a significant role in enhancing the performance, robustness, and efficiency of neural networks and sliding mode control strategies, making them more effective in a wide range of applications.

In this Special Issue, original research articles and reviews are welcome. The research areas may include (but not limited to) the following:

  • Symmetric control laws;
  • Symmetric structures in neural networks;
  • Sliding mode control with symmetry;
  • Symmetric state-space representations;
  • Symmetric stability analysis;
  • Symmetry-based fault detection and isolation;
  • Symmetric trajectories in robotics;
  • Symmetry in model predictive control;
  • Autonomous driving and vehicles;
  • Cyber physical systems.

Dr. Staniša Perić
Dr. Saša S. Nikolic
Guest Editors

Manuscript Submission Information

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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. Symmetry 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

  • symmetric control laws
  • symmetric structures in neural networks
  • sliding mode control with symmetry
  • symmetric state-space representations
  • symmetric stability analysis
  • symmetry-based fault detection and isolation
  • symmetric trajectories in robotics
  • symmetry in model predictive control
  • autonomous driving and vehicles
  • cyber physical systems

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Published Papers (1 paper)

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Research

28 pages, 24746 KiB  
Article
Non-Periodic Quantized Model Predictive Control Method for Underwater Dynamic Docking
by Tian Ni, Can Sima, Liang Qi, Minghao Xu, Junlin Wang, Runkang Tang and Lindan Zhang
Symmetry 2024, 16(10), 1392; https://doi.org/10.3390/sym16101392 - 18 Oct 2024
Viewed by 701
Abstract
This study proposed an event-triggered quantized model predictive control (ETQMPC) method for the dynamic docking of unmanned underwater vehicles (UUVs) and human-occupied vehicles (HOVs). The proposed strategy employed a non-periodic control approach that initiated the non-linear model predictive control (NMPC) optimization and state [...] Read more.
This study proposed an event-triggered quantized model predictive control (ETQMPC) method for the dynamic docking of unmanned underwater vehicles (UUVs) and human-occupied vehicles (HOVs). The proposed strategy employed a non-periodic control approach that initiated the non-linear model predictive control (NMPC) optimization and state sampling based on tracking errors and deviations from the predicted optimal state, thereby enhancing computing performance and system efficiency without compromising the control quality. To further conserve communication resources and improve information transfer efficiency, a quantitative feedback mechanism was employed for sampling and state quantification. The simulation experiments were performed to verify the effectiveness of the method, demonstrating excellent docking trajectory tracking performance, robustness against bounded current interference, and significant reductions in computational and communication burdens. The experimental results demonstrated that the method outperformed in the docking trajectory tracking control performance significantly improved the computational and communication performance, and comprehensively improved the system efficiency. Full article
(This article belongs to the Special Issue Symmetry in Control System Theory and Applications)
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