Composite learning tracking control for underactuated marine surface vessels with output constraints.

Area of Science:

• Marine Engineering
• Control Systems
• Robotics

Background:

• Underactuated marine surface vessels (MSVs) present significant control challenges due to complex dynamics and external disturbances.
• Existing control strategies often struggle with unknown system parameters and strict output constraints.

Purpose of the Study:

• To develop a robust composite learning control scheme for underactuated MSVs.
• To address unknown dynamics, time-varying external disturbances, and output constraints effectively.
• To ensure the stability and performance of the MSV control system.

Main Methods:

• Utilized the line-of-sight (LOS) approach to manage underactuation.
• Employed barrier Lyapunov functions to enforce output constraints.
• Implemented composite neural networks (NNs) with a serial-parallel estimation model (SPEM) for approximating unknown dynamics.
• Designed disturbance observers to estimate and compensate for external disturbances.
• Applied Lyapunov stability analysis to guarantee system boundedness.

Main Results:

• The proposed control scheme effectively handles unknown dynamics and time-varying disturbances.
• Output constraints were successfully maintained, preventing violation of performance limits.
• The system demonstrated uniform ultimate boundedness for all signals, confirming stability.
• Simulation results validated the efficacy of the composite learning control strategy.

Conclusions:

• The developed composite learning control scheme provides a robust solution for underactuated MSVs.
• The integration of NNs, barrier Lyapunov functions, and disturbance observers ensures reliable performance under challenging conditions.
• The findings contribute to the advancement of autonomous marine vehicle control systems.