Simultaneous modeling and backstepping control algorithm for trajectory tracking of underactuated USV based on...

Area of Science:

• Robotics and Control Systems
• Ocean Engineering
• Marine Autonomous Systems

Background:

• Underactuated unmanned surface vessels (USVs) face challenges in trajectory tracking due to complex ocean dynamics.
• Traditional models like the Fossen model may not fully capture the intricacies of USV maneuvering.
• Accurate parameter identification is crucial for effective USV control system design.

Purpose of the Study:

• To develop a high-precision maneuvering motion group (MMG) model for underactuated USVs.
• To propose a novel multi-innovation least squares (MILS) algorithm for accurate online identification of USV model parameters.
• To design a robust backstepping control algorithm enhanced by a nonlinear composite disturbance observer.

Main Methods:

• Established a high-precision maneuvering motion group (MMG) model for USVs.
• Developed a multi-innovation least squares (MILS) algorithm for online parameter identification using real-time data.
• Introduced virtual point position and intermediate states to simplify backstepping controller design.
• Designed a nonlinear composite disturbance observer to mitigate modeling errors and ocean disturbances.

Main Results:

• The MILS algorithm achieved high accuracy in identifying USV model parameters from real-time data.
• The proposed backstepping control algorithm, incorporating the disturbance observer, demonstrated enhanced stability and robustness.
• Simulation experiments validated the effectiveness and reliability of the developed trajectory tracking approach for underactuated USVs.

Conclusions:

• The novel MMG model and MILS identification algorithm provide a more accurate representation of USV dynamics.
• The integrated control strategy effectively addresses trajectory tracking challenges in complex ocean environments.
• The proposed method offers a reliable solution for autonomous navigation of underactuated USVs.