Full‐scale identification by use of self‐oscillations for overactuated marine surface vehicles

Abstract

SummaryController tuning and state estimation both benefit from knowledge about the dynamic parameters of the marine vessel. However, identifying these parameters can be a daunting task requiring precise open‐loop measurements or collection of many output data samples induced by persistently exciting inputs. Performing these experiments in real‐life conditions, every time payload of the vehicle changes, can be troublesome. Additional problems appear if the vessel is overactuated. This paper focuses on thruster modelling, actuator allocation and dynamic model identification for an overactuated marine vehicle. Firstly, we demonstrate a practical approach to mapping thrusters of an overactuated marine vehicle that in practice can generate different thrusts at identical inputs. Secondly, we address the issue of inverse actuator allocation for an overactuated surface marine platform and demonstrate a daisy‐chain approach for achieving proper thrust distribution during simultaneous motion in all controllable degrees of freedom (DoF). Finally, we describe the application of a robust identification by use of self‐oscillations (IS‐O) method to identify actuated DoF. While previous work focused on using IS‐O for identifying yaw DoF of a rudder‐actuated autonomous catamaran and thruster‐actuated micro‐remotely operated underwater vehicle, in this paper, we extend the approach to identifying surge and sway DoF. This closed‐loop identification procedure requires one experiment with four to five oscillations to completely identify inertial and nonlinear drag parameters of a marine vessel. Surge, sway and yaw DoFs of an overactuated autonomous surface marine vehicle PlaDyPos (developed at the Laboratory for Underwater Systems and Technologies) were identified using the IS‐O method during sea trials in real‐life conditions. Multiple experiments with varying initial settings were performed showing reproducibility of the identification procedure. Comparison of the results with the ordinary least squares identification procedure shows that root mean square error increase is negligible, especially if simplicity (explicit formulae for calculation of unknown parameters) and time parsimony of the IS‐O method are taken into account. © 2016 The Authors. International Journal of Adaptive Control and Signal Processing Published by John Wiley & Sons, Ltd.