Symbolic Regression for Marine Vehicles Identification∗

The mathematical models used in simulation must be reliable and trustworthy enough to describe the real systems with an appropriate accuracy. This simulation process is specially important in marine environment due to the changing environmental conditions, to the cost of the infrastructure needed to carry out tests, and to the need of calibration, deployment and recovery of the marine systems. If a reliable mathematical model of the vehicle is available, a part of the experimental tests can be avoided. In this paper we present a system identification technique based on genetic programming, the symbolic regression, to be applied on marine systems. In this sense, we show that it is possible to obtain a mathematical model of a ship for control purposes without the need of describing or knowing the model structure in advance, i.e., the identification itself provides the model structure that better describes the system. Thus, we can define a reliable black-box model that is computed in a simple way and where no many experimental data are needed. The model obtained is tested with additional data and manoeuvres to show its good performance and prediction ability.