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.

Experimental validation of the modeling and control of a multibody underwater vehicle manipulator system for sea mining exploration

“…Importance of the experimental identification for the control of underwater vehicles is well known in the community (Mišković, Nad, & Vukić, 2017; Smallwood & Whitcomb, 2003). With reference to the hydrodynamic model in (13), specific identification experiments have been designed aiming at identifying the unknown hydrodynamic parameters (drag coefficients, buoyancy, etc.).…”

Parameter identification of unmanned marine vehicle manoeuvring model based on extended Kalman filter and support vector machine

“…22 In addition, there are many other papers that have done relevant or similar research. [23][24][25][26][27][28][29][30] However, most UMV motion model parameter identification methods mainly focus on two-dimensional or threedimensional motion models, and most of them are very complex and computational, which have some limitations in practical application. The rudder response model is one of the most important manoeuvrability models of UMV, as heading response is one of the most important indexes in the course of UMV navigation.…”

“…22 In addition, there are many other papers that have done relevant or similar research. 23 –30…”

A new method for online identification of civil structures: Virtual synchronization

“…In the online methods, the estimation is done simultaneously with the measurements. [4][5][6] On the other hand, in the offline methods, the data is processed after the whole measurement is completed. (A different categorization can be found in the work of Sirca and Adeli.…”