The Application of PSO-AFSA Method in Parameter Optimization for Underactuated Autonomous Underwater Vehicle Control

Abstract

In consideration of the difficulty in determining the parameters of underactuated autonomous underwater vehicles in multi-degree-of-freedom motion control, a hybrid method that combines particle swarm optimization (PSO) with artificial fish school algorithm (AFSA) is proposed in this paper. The optimization process of the PSO-AFSA method is firstly introduced. With the control simulation models in the horizontal plane and vertical plane, the PSO-AFSA method is elaborated when applied in control parameter optimization for an underactuated autonomous underwater vehicle. Both simulation tests and field trials were carried out to prove the efficiency of the PSO-AFSA method in underactuated autonomous underwater vehicle control parameter optimization. The optimized control parameters showed admirable control quality by enabling the underactuated autonomous underwater vehicle to reach the desired states with fast convergence.