In this study, we consider the parameter estimation problem of a ship dynamics model. We consider two possible approaches to identify a continuous-time model from real data obtained on a river, where the presence of disturbances is a key issue. The first approach is identification through optimisation using a disturbance observer. The second approach corresponds to the refined instrumental variable method for linear parameter varying systems. In addition, we evaluate the accuracy of the parameter estimation through a sensitivity analysis. The obtained results show an improvement in the parameter estimates compared to identification procedures that do not consider the river disturbances. The application of the model for track-keeping control is also illustrated.

No abstract

No abstract

No abstract

The off-line estimation of the parameters of continuous-time, linear, timeinvariant transfer function models can be achieved straightforwardly using linear prefilters on the measured input and output of the system. The online estimation of continuous-time models with time-varying parameters is less straightforward because it requires the updating of the continuous-time prefilter parameters. This paper shows how such on-line estimation is possible by using recursive instrumental variable approaches. The proposed methods are presented in detail and also evaluated on a numerical example using both single experiment and Monte Carlo simulation analysis. In addition, the proposed recursive algorithms are tested using data from two real-life systems.