A robust optimal control by grey wolf optimizer for underwater vehicle-manipulator system

Area of Science:

• Robotics
• Control Systems Engineering
• Ocean Engineering

Background:

• Underwater vehicle-manipulator systems (UVMS) are critical for underwater operations.
• UVMS control is challenging due to modeling uncertainties and environmental disturbances.
• Existing control schemes require robust solutions for reliable performance.

Purpose of the Study:

• To propose a robust optimal control approach for UVMS.
• To enhance the stability and disturbance rejection capabilities of UVMS.
• To address nonlinear control problems in UVMS operating under lumped disturbances.

Main Methods:

• Developed a nonlinear dynamic model of the UVMS.
• Deduced the nonlinear model to a linear state-space model under lumped disturbances.
• Optimized H∞ controller parameters using the Grey Wolf Optimizer (GWO).
• Compared GWO with Ant Colony Optimization (ACO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO).
• Validated the control scheme using a hardware-in-the-loop (HIL) UVMS platform.

Main Results:

• The proposed GWO-optimized H∞ controller demonstrated improved performance.
• Achieved enhanced stability and superior disturbance rejection compared to other algorithms.
• The UVMS exhibited strong robustness against modeling uncertainties and external water disturbances.
• HIL experiments confirmed the effectiveness of the proposed control scheme.

Conclusions:

• The GWO-optimized H∞ control approach provides a robust solution for UVMS control.
• The method effectively mitigates the impact of modeling uncertainties and external disturbances.
• Hardware-in-the-loop experiments validate the practical applicability and performance of the proposed scheme.