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Modeling and Trajectory Tracking Model Predictive Control Novel Method of AUV Based on CFD Data.

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  • 1College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China.

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Summary

This study introduces a novel model predictive control (MPC) method using a genetic algorithm and ant colony optimization (GA-ACO) for autonomous underwater vehicle (AUV) trajectory tracking. The GA-ACO-MPC combined with dynamic sliding mode control (SMC) achieves robust and accurate underwater navigation.

Keywords:
GA-ACO algorithmautonomous underwater vehiclehydrodynamic analysismodel predictive controlnormal probability divisiontrajectory tracking

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Area of Science:

  • Robotics and Control Systems
  • Marine Engineering
  • Optimization Algorithms

Background:

  • Standard model predictive control (MPC) with constraints often fails to achieve global optimal solutions, particularly when using quadratic programming (QP).
  • Accurate trajectory tracking for autonomous underwater vehicles (AUVs) is critical for various marine applications.
  • External interference can significantly impact AUV performance and necessitate robust control strategies.

Purpose of the Study:

  • To propose a novel MPC method that overcomes the limitations of standard MPC in achieving global optimal solutions.
  • To apply this enhanced MPC, combined with dynamic sliding mode control (SMC), for precise trajectory tracking control of AUVs.
  • To validate the effectiveness and robustness of the proposed control strategy in a simulated underwater environment.

Main Methods:

  • Developed a novel model predictive control (MPC) approach integrating a population normal probability division genetic algorithm and ant colony optimization (GA-ACO).
  • Utilized ANSYS Fluent for Computational Fluid Dynamics (CFD) simulations to determine hydrodynamic coefficients for the AUV dynamic model.
  • Implemented dynamic sliding mode control (SMC) to refine control inputs and mitigate external disturbances, ensuring accurate velocity tracking.
  • Performed stability analysis using the Lyapunov method to confirm the controller's asymptotic stability.

Main Results:

  • The GA-ACO-MPC method successfully provided optimal velocity commands for AUV trajectory tracking.
  • Dynamic SMC effectively reduced the impact of external interference, enabling accurate velocity tracking.
  • Lyapunov stability analysis confirmed the asymptotic stability of the proposed controller.
  • MATLAB/Simulink simulations demonstrated the effectiveness and robustness of the GA-ACO-MPC with dynamic SMC for AUV trajectory tracking in a 3D underwater environment.

Conclusions:

  • The proposed GA-ACO-MPC combined with dynamic SMC offers a superior approach to AUV trajectory tracking compared to standard MPC methods.
  • The control strategy exhibits high effectiveness and robustness, making it suitable for real-world underwater navigation challenges.
  • This research contributes a novel and validated control solution for enhancing the performance of autonomous underwater vehicles.