PSO-Optimized LQR Controller for Underactuated AUV Depth Tracking

Autonomous underwater vehicles (AUVs) are essential for marine applications. However, ensuring stable operation in dynamic and uncertain underwater environments remains a critical challenge due to complex hydrodynamic behavior and unpredictable external disturbances. This paper presents a Particle Swarm Optimization optimized linear quadratic regulator (LQR) controller deployed in an underactuated AUV for a depth-tracking problem. The AUV model considers the non-linearities inherent to the system and the external uncertainties, such as hydrodynamic and environmental uncertainties. A double loop Adaptive Line of Sight (ALOS) LQR controller coupled with extended state observer is simulated and compared with the proposed controller. The proposed controller scheme yields faster settling time and lowers mean absolute error of the depth tracking error than the double loop strategies.