Event-triggered neuroadaptive predefined practical finite-time control for dynamic positioning vessels: A time-based...

Area of Science:

• Marine Engineering
• Control Systems Theory
• Robotics

Background:

• Dynamic positioning (DP) systems are crucial for marine vessels, facing challenges from internal and external uncertainties.
• Existing control methods may struggle with precise finite-time convergence and computational efficiency.

Purpose of the Study:

• To develop a novel predefined practical finite-time (PPFT) dynamic positioning control scheme for DP vessels.
• To address system uncertainties and reduce actuator wear through an event-triggered approach.

Main Methods:

• Utilizing a dynamic surface control framework combined with a time-based generator (TBG).
• Implementing a first-order filter and virtual parameter learning to manage virtual derivation and computational load.
• Introducing an event-triggering protocol to minimize actuator operating frequency and mechanical wear.

Main Results:

• The proposed event-triggered neuroadaptive PPFT control scheme ensures stability, validated by Lyapunov theorem.
• Offline determination of convergence time and control accuracy is achieved.
• Numerical examples confirm the efficiency of the proposed control approach.

Conclusions:

• The developed control strategy effectively handles uncertainties in DP vessels.
• The event-triggering mechanism successfully reduces actuator wear while maintaining performance.
• The PPFT control offers precise and timely positioning for DP applications.