Frequency domain identification of the Nomoto model to facilitate Kalman filter estimation and PID heading control of a patrol vessel

This paper presents a detailed frequency-domain system identification method applied to identify steering dynamics of a coastal patrol vessel using a data analysis software called CIFER. Advanced features such as the Chirp-Z transform and composite window optimization are used to extract high quality frequency responses. An accurate, robust and linear transfer function model is derived for yaw and roll dynamics of the vessel. To evaluate the accuracy of the identified model, time domain responses from a 45-45 zig-zag test are compared with the responses predicted by the identified model. The identified model shows excellent predictive capability and is well suited for simulation and controller design. Consequently, based on the identified linear model, a PID heading controller and a Kalman filter observer are constructed for estimating vessel motion-related parameters and filtering disturbances. The results show excellent tracking of pilot inputs in the presence of wave induced motions and forces.