_Applied_  
_sciences_  
_Article_  
**Ship Model Identification with Genetic Algorithm Tuning**  
**[PERSON]**  
Electrical Department, Gdynia Maritime University, ul. Morska 81-87, 81-225 Gdynia, Poland;  
[EMAIL_ADDRESS]  
**Featured Application: Good mathematical modeling, which is used for prediction, is one of the most important elements of the predictive controller. Even the best simulation model only approximates the behavior of the object. In real applications, usage of the model generated based on simulation data may cause unstable controller behavior due to model mismatch. One method to avoid this inconvenience is to linearize the plant model based on data gained during real-time experiments, not based on simulation results. However, one should then take into account external disturbances, whose appearance causes deterioration of the obtained model quality and extends the time of reliable data collection. The proposed algorithm is a method for linearized plant model tuning based on real measurements. It allows for model identification on the basis of the simulation data and its tuning in order to fit experimental data. This method quickens the model identification procedure and allows for its usage in real MPC (Model Predictive Control) applications. It was proved that the tuned model usage leads to elimination of high-frequency oscillations during its validation based on real-time experimental data.**  
**Abstract: Modeling is the most important component in predictive controller design. It should predict outputs precisely and fast. Thus, it must be adequate for the ship dynamics while having as simple a structure as possible. In a good ship model the standard deviation of a particular coefficient should not exceed 10% of its value. Fitting the validation data to 80% for short-term prediction and 65% for long-term prediction is treated as a declared benchmark for model usage in ship course predictive controller. Regularization was proposed to ensure better state-space models to fit the real ship dynamics and more accurate standard deviation value control. Usage of the simulation results and real-time trials, as model estimation and validation data, respectively, during the identification procedure is proposed. In the first step a predictive linear model is identified conventionally, and then coefficients are regularized, based on the validation data, using a genetic algorithm. Particular linearized model coefficient standard deviations were decreased from more than 100% of their values to approximately 5% of them using genetic algorithm tuning. Moreover, the proposed method eliminated model output signal oscillations, which were observed during the validation process based on experimental data, gained during ship trials. Improved mapping of ship dynamics was achieved. Fit to validation data increased from 71% and 54% to 89% and 76%, respectively, for short-term and long-term prediction. The proposed method, which may be applied to real applications, is easily applicable and reliable. The tuned model is sufficiently suited to plant dynamics and may be used for future predictive control purposes.  
**Keywords: plant identification; model regularization; genetic algorithm; state-space model; ship model**