## 1 Introduction  
Modeling is the most important element in the model-based control techniques (e.g., Model Predictive Control). Its structure and way of identification depend on future applications (simulation or prediction), identification of data form (time- or frequency-domain), required model form (linear or nonlinear), and control type (discrete or continuous). The identification procedure is a demanding and time-consuming issue, and it should be done as precisely as possible. Therefore, it is still a growing branch in the field of control. A ship is a highly nonlinear MIMO (Multiple Input Multiple Output) control object, which is characterized by large inertia and susceptibility to external disturbances such as wind and waves. There is a variety of developed ship models for the future control process. In the identification theory they are divided into two main groups: linear, which are accurate only near the set point, and nonlinear, which are proper for a wider ship speed range. Nonlinear ones are based on Kalman Filters [1], identified during model tests as the nonlinear mathematical model parameters [2], based on the backstepping procedure and the tuning design method using the complex model of Wagner-Smith [3]. These nonlinear models may be also based on neural networks, e.g., conventional [4] or probabilistic [5] ones. Linear ship models, for the future application in the ship automatic control system, are identified as a second-order linear Nomoto model based on zig-zag tests [6], input-output time domain data obtained from nonlinear ship dynamics, mathematical models [7], or as linear incremental models [8]. Synthesis of a predictive ship control system forces the use of a predictive plant model [9], which in practical applications is linearized to minimize the controller computational effort. Deterministic artificial intelligence model reparametrization was presented in [10]. In this publication methodology, which automates control and eliminates tuning completely, the method is used to search for optimal parameter values and then to update the constants and constituent constants in rudder commands. Thus, this method may be an alternative for model predictive control because it easily uses artificial intelligence based reparametrization instead of model parameters estimation. System identification, applied to get a reliable ship maneuvering model, may be done in various configurations and based on a variety of algorithms. There are three types of models that are widely applied to the autonomous ship control: white-box (WBM), grey-box (GBM), and black-box (BBM) models. WBM is based on the deterministic equations and knowledge about the ship dynamics and kinematics. Due to nonlinearities, it is hard to identify a reliable ship's WBM. BBMs are built only based on the input-output data sets. No knowledge about the system structure and parameters is required. This group of models includes neural models [11] and models established by using support vector machines [12]. Black-box modeling is useful when identifying nonlinear ship models. GBM's structure is known, and parameters are estimated on the basis of gained input-output data. GBM combines advantages of the WBM and BBM. The Nomoto model is often used to model linearized ship dynamics for control purposes. Its coefficients are obtained via optimization procedures, e.g., genetic algorithms [13], particle swarm optimization [14], or frequency-domain identification procedure [15]. Nomoto models are valid for small rudder angles and low frequencies of rudder action [16]. They are also applicable for pod-driven USV [17], but they have a lower performance in output prediction compared to state-space models, whose identification procedure is based on prediction error minimization [8]. Therefore, state-space canonical GBMs are used in model-based control. Researchers take notable effort to linearize ship dynamics. Proper linear vessel model identification requires a definition of the acceptable set-point deviations.