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Robust Adaptive Self-Structuring Neural Network Bounded Target Tracking Control of Underactuated Surface Vessels.

Haitao Liu1,2, Jianfei Lin1, Guoyan Yu1,2

  • 1School of Mechanical and Power Engineering, Guangdong Ocean University, Zhanjiang 524088, China.

Computational Intelligence and Neuroscience
|December 31, 2021
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Summary
This summary is machine-generated.

This study presents a novel robust adaptive controller for underactuated surface vessels, enhancing target-tracking accuracy despite model uncertainties and disturbances. The method optimizes neural network structure for efficiency and stability.

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Area of Science:

  • Robotics and Control Systems
  • Marine Engineering
  • Artificial Intelligence

Background:

  • Underactuated surface vessels face challenges in target tracking due to model uncertainties and external disturbances.
  • Accurate estimation of target velocity and system nonlinearities is crucial for effective control.
  • Input saturation can limit the performance of conventional control strategies.

Purpose of the Study:

  • To develop a robust adaptive controller for underactuated surface vessels capable of handling model uncertainties and unknown disturbances.
  • To enhance target-tracking performance and avoid input saturation.
  • To estimate uncertain terms and target velocity using limited sensor data.

Main Methods:

  • A composite robust adaptive self-structuring neural-network-bounded controller is proposed.
  • An extended state observer is utilized to estimate the uncertain nonlinear term and target velocity.
  • An adaptive self-structuring neural network approximates uncertainties, optimizing its structure online to reduce computational load.
  • Cascade stability theorem is employed to analyze the input-to-state stability of the closed-loop system.

Main Results:

  • The proposed controller effectively improves system performance in target tracking.
  • The method successfully avoids input saturation.
  • The extended state observer accurately estimates the uncertain nonlinear term and target velocity.
  • The adaptive neural network efficiently approximates uncertainties while optimizing its structure.
  • Simulation results validate the effectiveness of the proposed approach.

Conclusions:

  • The developed controller offers a robust and efficient solution for target tracking in underactuated surface vessels.
  • The combination of an extended state observer and an adaptive self-structuring neural network provides a powerful tool for handling system uncertainties.
  • The proposed method demonstrates significant potential for real-world applications in marine navigation and control.