Depth-Keeping Control for a Deep-Sea Self-Holding Intelligent Buoy System Based on Inversion Time Constraint...

Area of Science:

• Marine engineering
• Control systems theory
• Optimization algorithms

Background:

• Deep-sea intelligent buoys require precise depth control for stable operation.
• Traditional controllers struggle with optimal parameter tuning for depth-holding.
• Nonlinear disturbance observers (NDO) are foundational for stability strategies.

Purpose of the Study:

• To develop an optimized depth controller for the deep-sea self-holding intelligent buoy (DSIB).
• To enhance the performance of the inversion time-constraint stability strategy (ITCS) through advanced optimization.
• To ensure stable operation conditions for marine sensors carried by the DSIB.

Main Methods:

• A quantum-behaved genetic algorithm (QGA) was proposed for initial parameter optimization.
• A hybrid genetic algorithm and chaos particle swarm optimization (GACPSO) was developed to improve search speed and accuracy.
• Simulations and hardware-in-the-loop experiments validated the proposed controllers.

Main Results:

• The GACPSO-optimized ITCS depth controller demonstrated superior stability.
• The proposed controller achieved smaller steady-state errors and reduced settling times.
• Effectiveness and feasibility were confirmed through rigorous testing.

Conclusions:

• The GACPSO-ITCS controller offers significant improvements in deep-sea buoy depth control.
• This research provides a robust solution for stable marine sensor deployment.
• The optimized controller ensures reliable performance in challenging marine environments.