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.

No abstract

No abstract