Symmetry/Asymmetry in Intelligent Control System

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

Deadline for manuscript submissions: 31 March 2026 | Viewed by 563

Special Issue Editor


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Guest Editor
Department of Mechanical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok 10330, Thailand
Interests: control; dynamics; observer; estimation

Special Issue Information

Dear Colleagues,

Intelligent control systems have become indispensable in a wide range of modern engineering applications, from autonomous vehicles and robotics to industrial automation and smart energy systems. A key, yet often underexplored, aspect in this field is the role of symmetry and asymmetry—both in the system dynamics and in the structure of control algorithms. Symmetry in control systems can enhance stability, simplify modeling, and reduce computational cost. Conversely, asymmetry often arises naturally in real-world systems—due to environmental disturbances, system constraints, or learning-based adaptation—and requires robust control strategies to handle such deviations. Understanding and leveraging these symmetrical or asymmetrical characteristics can lead to significant improvements in system performance, robustness, and adaptability.

This Special Issue aims to gather original research and review articles that explore the theoretical foundations, algorithmic developments, and practical applications of symmetry/asymmetry in intelligent control systems. Topics of interest include, but are not limited to, the following:

  • Symmetry in system dynamics and control design;
  • Asymmetry in adaptive or learning-based controllers;
  • Symmetry-aware observer and estimation techniques;
  • Role of symmetry/asymmetry in fault diagnosis and fault-tolerant control;
  • Intelligent control in systems with unbalanced structures or uncertainties;
  • Applications in robotics, autonomous systems, energy systems, etc.

We invite researchers and practitioners to contribute articles that demonstrate how symmetry or asymmetry can be systematically incorporated or addressed to advance the state of intelligent control systems.

We look forward to your valuable contributions.

Dr. Gridsada Phanomchoeng
Guest Editor

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Keywords

  • symmetry in control systems
  • asymmetric control design
  • intelligent control
  • adaptive control
  • nonlinear systems
  • robust control
  • learning-based control
  • observer design
  • fault-tolerant control
  • autonomous systems

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Published Papers (1 paper)

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Research

19 pages, 7025 KB  
Article
Physical Information-Driven Optimization Framework for Neural Network-Based PI Controllers in PMSM Servo Systems
by Zhiru Song and Yunkai Huang
Symmetry 2025, 17(9), 1474; https://doi.org/10.3390/sym17091474 - 7 Sep 2025
Viewed by 304
Abstract
In industrial scenarios, the control of permanent magnet synchronous servo motors is mostly achieved with proportional–integral controllers, which require manual adjustment of control parameters. At the same time, the performance of the servo system is usually disturbed by internal characteristic changes, load changes, [...] Read more.
In industrial scenarios, the control of permanent magnet synchronous servo motors is mostly achieved with proportional–integral controllers, which require manual adjustment of control parameters. At the same time, the performance of the servo system is usually disturbed by internal characteristic changes, load changes, and external factors. Therefore, preset control parameters may not achieve the desired optimal performance. Many scholars use intelligent algorithms, such as neural networks, to adaptively tune control parameters. However, the offline pre-training of neural networks is often time- and resource-consuming. Due to the lack of a model pre-training process in the neural network online self-tuning process, randomly setting the initial network weight seriously affects the position tracking performance of the servo control system in the start-up phase. In this paper, the physical model and the traditional frequency domain-tuning method of the three-closed-loop permanent magnet synchronous servo system are analyzed. Combined with the neural network PI control parameter self-tuning method and physical symmetry, a physical information-driven optimization framework is proposed. To demonstrate its superiority, the neural network PI controller and the proposed optimization framework are used to control the single-axis sine wave trajectory. The results show that the optimization framework proposed can effectively improve the position tracking control performance of the servo control system in the start-up phase by setting the threshold of the servo control parameters, reduce the position tracking control error to 0.75 rads in the start-up phase, and reduce the position tracking drop caused by a sudden load by 25%. This method achieves the independent optimization adjustment of control parameters under position tracking control, providing a reference for the intelligent control of permanent magnet synchronous servo motors. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Intelligent Control System)
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