Symmetry and Asymmetry in Automatic Control

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 1693

Special Issue Editors


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Guest Editor
Institute of Robotics and Industrial Informatics, Spanish National Research Council (CSIC) and the Technical University of Catalonia (UPC), Barcelona, Spain
Interests: fault diagnosis; neuro-fuzzy systems; artificial neural network; zonotopic observers

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Guest Editor
TURIX-Dynamics Diagnosis and Control Group, Tecnológico Nacional de Mexico, I.T. Tuxtla Gutiérrez, Tuxtla Gutiérrez, Mexico
Interests: robotics; intelligent control; fault diagnosis; machine learning; computational intelligence

Special Issue Information

Dear Colleagues,

The study of symmetry and asymmetry in automatic control plays a fundamental role in the design, stability, and performance of complex dynamic systems. Symmetry often simplifies control strategies, leading to predictable and well-balanced system responses, while asymmetry introduces variability that can enhance adaptability and robustness in uncertain environments. This Special Issue aims to explore recent advances in the modeling, analysis, and implementation of symmetric and asymmetric control approaches across diverse engineering applications.

We welcome submissions on topics including, but not limited to, the following:

  • Symmetric and asymmetric control strategies;
  • Robust and adaptive control methods for asymmetric systems;
  • Nonlinear and optimal control approaches;
  • Fault-tolerant control in complex dynamic systems;
  • Cyber-physical systems with asymmetric control constraints;
  • Applications in robotics, aerospace, and industrial automation.

We encourage the submission of original research articles and review papers addressing theoretical developments, novel methodologies, and real-world case studies.

We look forward to receiving your contributions.

Dr. Esvan de Jesús Pérez-Pérez
Dr. Ildeberto Santos-Ruiz
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.

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Keywords

  • symmetric control
  • asymmetric control
  • robust control
  • adaptive control
  • nonlinear control
  • optimal control
  • fault-tolerant control
  • cyber-physical systems
  • robotics and automation
  • aerospace control

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Published Papers (1 paper)

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Research

29 pages, 1525 KB  
Article
Neural Network Auto-Design Algorithm for Urban Travel Time Prediction
by Eduardo Chandomí-Castellanos, Elías N. Escobar-Gómez, Jorge Iván Bermúdez Rodríguez, José-Roberto Bermúdez, Julio-Alberto Guzmán-Rabasa, Ildeberto Santos-Ruiz and Esvan-Jesús Pérez-Pérez
Symmetry 2026, 18(3), 442; https://doi.org/10.3390/sym18030442 - 4 Mar 2026
Viewed by 627
Abstract
This paper proposes to estimate the travel time at each edge of an urban street network using Artificial Neural Networks (ANNs). To improve the ANN performance and minimize errors in manual design, an Algorithm Auto-Design ANN Topology (A-DANNT) is introduced that automatically determines [...] Read more.
This paper proposes to estimate the travel time at each edge of an urban street network using Artificial Neural Networks (ANNs). To improve the ANN performance and minimize errors in manual design, an Algorithm Auto-Design ANN Topology (A-DANNT) is introduced that automatically determines the most suitable architecture for regression problems. The methodology implements an algorithm based on Tabu Search, called the Best R-Value Determination algorithm (BR-vD), which optimizes the topology obtaining a lower Mean Square Error (MSE) and a higher correlation coefficient. The process is developed in three phases: first, the variables that impact the travel time are analyzed; then, the proposed algorithm is used to find the best topology; and finally, the travel times are estimated. The proposal is evaluated in two case studies: in the first, the algorithm automatically designs the architecture, and a 0.99366 correlation coefficient is achieved between the results and the objectives. In the second case, the performance of the algorithm is compared with a fuzzy travel time model, achieving a 0.99898 correlation coefficient. In both cases, the proposed algorithm is capable of designing topologies with coefficients greater than 0.99 and Mean Absolute Errors (MAEs) of 3.2765 and 0.6957 s, respectively. Full article
(This article belongs to the Special Issue Symmetry and Asymmetry in Automatic Control)
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