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This paper describes a control algorithm that uses an adaptive neural-fuzzy network to control the depth of an elongated cylindrical body class of autonomous underwater vehicles (AUVs) operating in a marine environment with many uncertainties. After successfully synthesizing the control algorithm, a Particle Swam Optimization (PSO) algorithm is added to determine the controller parameters. The controller parameters found based on the PSO algorithm have made the system's stability more sustainable. Finally, the simulation results of the depth control of the AUV in the vertical plane are provided to verify the effectiveness of the adaptive neural fuzzy network controller (ANFNC) algorithm combined with PSO.

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Optimization of Adaptive Neural-Fuzzy Network Controller Using Particle Swam Optimization Algorithm to Depth Control for AUV

  • 会议论文

  • 发表日期：

    2026年

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  • Nguyen Van Hoa,
  • Pham Van Tuan,
  • Nguyen Quang Vinh,
  • Nguyen Tat Tuan

摘要

This paper describes a control algorithm that uses an adaptive neural-fuzzy network to control the depth of an elongated cylindrical body class of autonomous underwater vehicles (AUVs) operating in a marine environment with many uncertainties. After successfully synthesizing the control algorithm, a Particle Swam Optimization (PSO) algorithm is added to determine the controller parameters. The controller parameters found based on the PSO algorithm have made the system's stability more sustainable. Finally, the simulation results of the depth control of the AUV in the vertical plane are provided to verify the effectiveness of the adaptive neural fuzzy network controller (ANFNC) algorithm combined with PSO.