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 December 2026 | Viewed by 2411

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. In recent years, the field has evolved from classical model-based methodologies toward hybrid paradigms that integrate data-driven and learning-based techniques. Within this evolution, a key—yet often underexplored—aspect is the role of symmetry and asymmetry in both system dynamics and control architectures. Symmetry in control systems can enhance stability, simplify modeling, enable structure-preserving design, and reduce computational complexity. Conversely, asymmetry naturally arises in real-world systems due to disturbances, constraints, heterogeneous agents, structural imbalance, or learning-induced adaptations. Such asymmetries necessitate robust, adaptive, and learning-enabled control strategies. A systematic understanding of how symmetry and asymmetry influence stability, performance, robustness, and scalability is therefore essential for advancing next-generation intelligent control systems.

This Special Issue aims to gather original research and review articles addressing theoretical developments, algorithmic innovations, and practical applications related to symmetry/asymmetry in intelligent and learning-based control systems. Contributions from both classical and data-driven control perspectives are welcome.

Topics of interest include, but are not limited to, the following:

  • Symmetry in system dynamics and control design;
  • Asymmetry in adaptive, robust, or learning-based controllers;
  • Symmetry-aware observer and state estimation techniques;
  • Learning-based and data-driven control (e.g., reinforcement learning, neural network control);
  • Hybrid model-based and learning-integrated control frameworks;
  • Distributed and multi-agent control with structural symmetry or asymmetry;
  • Role of symmetry/asymmetry in fault diagnosis and fault-tolerant control;
  • Intelligent control of systems with uncertainties, constraints, or unbalanced structures;
  • Applications in robotics, autonomous systems, energy systems, and cyber-physical systems.

We invite researchers and practitioners to contribute high-quality articles that demonstrate how symmetry or asymmetry—whether explicitly modeled or implicitly learned—can be systematically incorporated to advance the theory and practice 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
  • reinforcement learning for control
  • neural network control
  • data-driven control
  • observer design
  • fault-tolerant control
  • distributed control
  • autonomous systems

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Published Papers (2 papers)

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Research

21 pages, 5124 KB  
Article
Adaptive Fault-Tolerant Super Twisting Control Design Based on K Function for Symmetric Manipulators
by Haicheng Wan, Yutao Wang, Ping Wang and Wendong Li
Symmetry 2025, 17(11), 1978; https://doi.org/10.3390/sym17111978 - 15 Nov 2025
Viewed by 650
Abstract
In this study, we introduce a novel adaptive fault-tolerant sliding mode control strategy for the finite-time control of symmetric robotic manipulators subjected to uncertainties, disturbances and actuator failures. Firstly, we design a novel type of sliding mode manifold termed Practical Fast Terminal Sliding [...] Read more.
In this study, we introduce a novel adaptive fault-tolerant sliding mode control strategy for the finite-time control of symmetric robotic manipulators subjected to uncertainties, disturbances and actuator failures. Firstly, we design a novel type of sliding mode manifold termed Practical Fast Terminal Sliding Mode (P-FTSM). P-FTSM exhibits the capability to accelerate convergence speed while ensuring the finite-time convergence of the system. Subsequently, the P-FTSM is integrated with the super-twisting algorithm (STA) to mitigate the chattering of control input. Additionally, a novel K function is introduced to serve as the gain of the STA. This strategy, which does not require knowledge of the upper bound of the disturbance and fault information, ensures that the gain is tuned according to the disturbance and fault variations, mitigating the adverse effects of high gain and further weakening of the chattering. Simulation results on a two-link symmetric manipulator verify that the proposed method achieves outstanding quantitative performance. The proposed method achieves convergence times of 0.22 and 0.12 s for the joint errors, with root mean square errors (RMSE) of 0.036 and 0.095. The integral absolute errors (IAE) are 0.049 and 0.086, and the total control energy is 943.46. The total variations (TV) of the control signals are 2.86×103 and 1.64×103, indicating effectively suppressed chattering. Overall, the proposed strategy ensures high precision, rapid convergence, and strong fault-tolerant capability. Full article
(This article belongs to the Special Issue Symmetry/Asymmetry in Intelligent Control System)
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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
Cited by 1 | Viewed by 1098
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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