Adaptive learning control of nonlinear underwater robots: achieving repetitive tracking without parameterization.

Area of Science:

• Robotics
• Control Systems
• Marine Engineering

Background:

• Underwater robots require precise control for marine exploration.
• Non-parameterized nonlinear systems pose significant control challenges.
• Existing adaptive iterative learning control (ILC) methods may lack robustness or efficiency.

Purpose of the Study:

• To develop an adaptive ILC strategy for non-parameterized nonlinear underwater robots.
• To enhance tracking precision and robustness against system uncertainties.
• To create a memory-efficient controller with minimal adaptive variables.

Main Methods:

• An adaptive iterative learning control (ILC) approach utilizing difference-differential principles.
• Dynamic adjustment of control parameters through combined difference and differential links.
• A compact controller structure with two adaptive variables for memory conservation.

Main Results:

• The proposed adaptive ILC method demonstrated high-precision tracking capabilities.
• The controller exhibited significant robustness against system uncertainties and stochastic disturbances.
• Numerical simulations confirmed superior performance compared to existing adaptive ILC schemes in tracking precision and convergence speed.

Conclusions:

• The developed difference-differential based adaptive ILC is effective for nonlinear underwater robots.
• The controller offers improved tracking accuracy and faster convergence.
• This method contributes to advancing autonomous marine exploration capabilities.