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<p>Owing to the intricate nature of the internal environment within oil-immersed transformers, there exist significant requirements for the spatial agility, operational capability, and stability of inspection tools. To tackle this challenge, a research proposal is introduced, proposing the utilization of a spherical underwater robot for inspecting the interior of oil-immersed transformers. The study develops an underactuated dynamics model for the spherical underwater robot, which partitions the robot into independent attitude subsystems and position subsystems tightly interconnected with the attitude subsystems. Considering the hierarchical nature of these two subsystems, a hybrid dual-loop control strategy is suggested. The inner loop attitude subsystem is governed by an enhanced sliding mode control integrating active disturbance rejection control, whereas the control methodology for the outer loop position subsystem incorporates radial basis function neural network sliding mode adaptive control. The stability of this controller is validated through Lyapunov theory analysis. Lastly, MATLAB simulation experiments are performed, demonstrating that the developed controller showcases excellent autonomy, disturbance rejection capability, and maneuverability.</p>

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Dual-loop robust control of an inspection robot for oil-immersed transformers

  • 期刊论文

  • 发表日期：

    2025年9月2日

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  • Hong Zhaobin,
  • Sun Wenhong,
  • Chen Shuixuan,
  • Wang Shaomin

摘要

Owing to the intricate nature of the internal environment within oil-immersed transformers, there exist significant requirements for the spatial agility, operational capability, and stability of inspection tools. To tackle this challenge, a research proposal is introduced, proposing the utilization of a spherical underwater robot for inspecting the interior of oil-immersed transformers. The study develops an underactuated dynamics model for the spherical underwater robot, which partitions the robot into independent attitude subsystems and position subsystems tightly interconnected with the attitude subsystems. Considering the hierarchical nature of these two subsystems, a hybrid dual-loop control strategy is suggested. The inner loop attitude subsystem is governed by an enhanced sliding mode control integrating active disturbance rejection control, whereas the control methodology for the outer loop position subsystem incorporates radial basis function neural network sliding mode adaptive control. The stability of this controller is validated through Lyapunov theory analysis. Lastly, MATLAB simulation experiments are performed, demonstrating that the developed controller showcases excellent autonomy, disturbance rejection capability, and maneuverability.