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Research on the Movement Control of Non-Traditional Six Propeller ROV Based on BP-PID Algorithm

  • Shiyue Cui
  • , Xinshuo Li
  • , Zhihao Dong
  • , Yong Cao
  • Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

In order to solve the problem that traditional PID control is difficult to adapt to the highly nonlinear and strongly coupled characteristics of ROV, this research, focusing on the motion control mechanism of ROV, constructs a non-traditional six-propeller-driven ROV physical platform and designs a PID control algorithm based on BP neural network. Through dynamic modeling and physical tests, the control effects of BP-PID and traditional PID are compared, and the improvement of system response characteristics and performance is evaluated. The experimental results indicate that the BP-PID control algorithm has significant advantages in enhancing the stability and response speed of the ROV system. After optimization, Pitch stabilizes after approximately 40 iterations, with the steady-state error controlled at about 4° and the maximum deviation also at about 4°; Roll stabilizes after 55 iterations, with the steady-state error at about 2° and the maximum deviation at 17°; Yaw stabilizes after 50 iterations, with the steady-state error at about 10° and the maximum deviation at 13°. These data suggest that the optimized system shows a shorter adjustment time before reaching the steady state and a significantly reduced steady-state error. In terms of dynamic performance, the overshoot of the system optimized by BP-PID is significantly reduced, and the recovery time is shortened, demonstrating superior anti-interference ability and robustness.

Original languageEnglish
Title of host publication2025 IEEE 3rd International Conference on Control, Electronics and Computer Technology, ICCECT 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1433-1439
Number of pages7
ISBN (Electronic)9798331518554
DOIs
StatePublished - 2025
Event3rd IEEE International Conference on Control, Electronics and Computer Technology, ICCECT 2025 - Jilin, China
Duration: 25 Apr 2025 → 27 Apr 2025

Publication series

Name2025 IEEE 3rd International Conference on Control, Electronics and Computer Technology, ICCECT 2025

Conference

Conference3rd IEEE International Conference on Control, Electronics and Computer Technology, ICCECT 2025
Country/TerritoryChina
CityJilin
Period25/04/25 → 27/04/25

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • BP Neural Network
  • PID Control
  • ROV

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