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Experimental Evaluation on Depth Control Using Improved Model Predictive Control for Autonomous Underwater Vehicle

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This study enhances model predictive control (MPC) for autonomous underwater vehicles (AUVs) by introducing an error-varied weighting matrix and trajectory re-planning. These improvements significantly reduce settling time and trajectory tracking errors.

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Area of Science:

  • Robotics
  • Control Systems Engineering
  • Ocean Engineering

Background:

  • Model Predictive Control (MPC) is increasingly applied to Autonomous Underwater Vehicles (AUVs).
  • Existing research often focuses on simulations or basic MPC applications for AUVs.
  • There is a need for improved MPC strategies to enhance AUV performance in real-world scenarios.

Purpose of the Study:

  • To improve the performance of MPC for AUVs by modifying the control increment vector weighting matrix.
  • To address trajectory tracking lags in AUVs through a novel trajectory re-planning method.
  • To validate the effectiveness of the enhanced MPC approach through experimental depth control.

Main Methods:

  • Implemented an error-varied coefficient to adjust the control increment vector weighting matrix in MPC.
  • Developed a simple trajectory re-planning strategy by advancing the desired trajectory point.
  • Conducted experimental depth control tests on an AUV to evaluate the proposed methods.

Main Results:

  • The real-time adjusted weighting matrix reduced settling time by approximately 2 seconds for a 1m step trajectory.
  • The trajectory re-planning method decreased the average absolute error by about 15%.
  • The trajectory re-planning method reduced the standard deviation of error by approximately 17%.

Conclusions:

  • The proposed error-varied weighting matrix effectively reduces settling time in AUV depth control.
  • The simple trajectory re-planning method significantly improves AUV trajectory tracking accuracy.
  • The enhanced MPC approach demonstrates feasibility and effectiveness for AUV control applications.