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Disturbance observer-based adaptive reinforcement learning for perturbed uncertain surface vessels.

Van Tu Vu1, Thanh Loc Pham2, Phuong Nam Dao2

  • 1Haiphong University, Haiphong, Viet Nam.

ISA Transactions
|April 22, 2022
PubMed
Summary

This study introduces an adaptive/approximate reinforcement learning (ARL) control for uncertain surface vessels (SVs) with external disturbances. The novel approach enhances tracking performance and stability without traditional separation techniques.

Keywords:
Adaptive/approximate reinforcement learning (ARL)Disturbance observer (DO)Lyapunov stability theoryOptimal controlSurface vessels (SVs)

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Area of Science:

  • Robotics and Control Systems
  • Marine Engineering
  • Artificial Intelligence

Background:

  • Surface vessels (SVs) face complex control challenges due to external disturbances and model uncertainties.
  • Conventional control methods often require model separation techniques, limiting their applicability.
  • Achieving precise tracking and convergence in SV control remains a significant research problem.

Purpose of the Study:

  • To develop an optimal control strategy for uncertain surface vessels (SVs) incorporating disturbance observer (DO) based adaptive/approximate reinforcement learning (ARL).
  • To enhance tracking performance and system stability by reducing the attraction region of tracking errors.
  • To validate the effectiveness of the proposed ARL control strategy through simulations and examples.

Main Methods:

  • Design of a disturbance observer (DO) to estimate and compensate for external disturbances acting on SVs.
  • Application of adaptive/approximate reinforcement learning (ARL) for optimal control, utilizing a single dynamic equation for SVs.
  • Development of an equivalent system to address the non-autonomous property of the closed tracking error SV model.
  • Stability and convergence analysis using Lyapunov functions, considering the optimal function and estimated actor/critic weight errors.

Main Results:

  • The proposed ARL-based optimal control significantly reduces the tracking error attraction region through an effective disturbance observer.
  • The control strategy eliminates the need for conventional separation techniques, simplifying the control design for SVs.
  • Stability and convergence of the closed-loop system are rigorously proven using Lyapunov stability theory.
  • Simulation examples demonstrate the superior performance and effectiveness of the developed control strategy.

Conclusions:

  • The adaptive/approximate reinforcement learning (ARL) based optimal control with a disturbance observer (DO) offers a robust solution for uncertain surface vessel (SV) tracking.
  • This approach provides enhanced tracking accuracy and system stability, outperforming conventional methods.
  • The study confirms the practical applicability and effectiveness of the proposed control strategy for marine applications.