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Experimental Evaluation on Depth Control Using Improved Model Predictive Control for Autonomous Underwater Vehicle (AUVs).

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Optimization of the Energy Consumption of Depth Tracking Control Based on Model Predictive Control for Autonomous

Feng Yao1, Chao Yang2, Mingjun Zhang3

  • 1College of Mechanical and Electrical Engineering, Harbin Engineering University, Nangang District, Harbin 150001, China. yaofeng@hrbeu.edu.cn.

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|January 10, 2019
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Summary

This study introduces an improved Model Predictive Control (MPC) for autonomous underwater vehicles (AUVs) to reduce energy consumption during trajectory tracking. The enhanced control strategy optimizes energy usage for longer, more efficient underwater missions.

Keywords:
autonomous underwater vehiclescost functionenergy consumption optimizationmodel predictive controltrajectory tracking

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Area of Science:

  • Robotics
  • Control Systems
  • Ocean Engineering

Background:

  • Autonomous Underwater Vehicles (AUVs) have limited onboard energy for long-term missions.
  • Reducing energy consumption is critical for enhancing AUV operational capabilities in complex marine environments.

Purpose of the Study:

  • To develop and validate an energy-efficient trajectory tracking control strategy for AUVs.
  • To minimize energy consumption in AUVs through an optimized Model Predictive Control (MPC) approach.

Main Methods:

  • Utilized a state-space model of AUVs for trajectory tracking control.
  • Introduced a quadratic energy term into the cost function of Model Predictive Control (MPC).
  • Analyzed the stability implications of the modified MPC cost function.

Main Results:

  • The proposed MPC strategy effectively reduces energy consumption in AUVs.
  • Simulation results for depth tracking control demonstrate the feasibility of the energy optimization method.
  • The addition of the quadratic energy term in the cost function enhances energy efficiency.

Conclusions:

  • The improved MPC approach offers a viable solution for energy consumption optimization in AUVs.
  • This method contributes to extending the endurance and effectiveness of AUVs in marine exploration and monitoring.