Parameter identification of an open-frame underwater vehicle based on numerical simulation and quantum particle swarm optimization

Accurate parameter identification of underwater vehicles is of great significance for their controller design and faultdiagnosis. Some studies adopt numerical simulation methods to obtain the model parameters of underwatervehicles, but usually only conduct decoupled single-degree-of-freedom steady-state numerical simulations to identify resistance parameters. In this paper, the velocity response is solved by applying a force (or torque) to the underwater vehicle based on the overset grid and Dynamic Fluid-Body Interaction model of STAR-CCM+, solvingfor the velocity response of an underwater vehicle in all directions in response to propulsive force (or moment)inputs. Based on the data from numerical simulations, a parameter identification method using quantum particleswarm optimization is proposed to simultaneously identify inertia and resistance parameters. By comparing the forward velocity response curves obtained from pool experiments, the identified vehicle model's mean square error of forward velocity is less than 0.20%, which is superior to the steady-state simulation method and particle swarmoptimization and genetic algorithm approaches.