Identification of the equivalent linear dynamics and controller design for an unmanned underwater vehicle

This paper investigates the applicability of frequency-domain system identification technique to achieve the equivalent linear dynamics of an autonomous underwater vehicle for control design purposes. Frequency response analysis is performed on the nonlinear and coupled dynamics of the vehicle, utilizing the CIFER (R) software to extract a reduced-order model in the form of equivalent transfer functions. Advanced features such as chirp-z transform, composite window optimization, and conditioning are-employed to achieve high quality and accurate frequency responses. A particular frequency-sweep input is implemented to the nonlinear simulation model to achieve pole-zero transfer functions for yaw and pitch motions that were previously developed by the perturbed equations of motion. To evaluate the accuracy of the identified models, zig-zag test data are compared with the predicted responses for both identified and linearized models in time domain. The results show that the identified models perform significantly well in the presence of noise and model uncertainties with the maximum error of 12%, thanks to the precise spectral analysis. Proportional-integral controllers are designed based on the extracted models and tracking performance is experimentally demonstrated by several test results that show the ability of the vehicle to navigate autonomously and follow the GPS waypoints with reasonable accuracy.