Modelling and identification of a twin hull-based autonomous surface craft

In this paper, the process and results of a parameter identification task for a maritime unmanned surface vehicle are presented. The system has been modeled using state of the art methodology, and the parameters have been estimated via a weighted least square optimization approach. The required measurement data have been taken from various maneuvre trials. For solving the optimization problem, a Particle Swarm programming approach has been used, which has reliably found the global minimum of the cost function. The results show excellent agreement between measured and simulated data.