Modeling and identification of open-frame variable configuration unmanned underwater vehicles

Caccia, Massimo; Indiveri, Giovanni; Veruggio, G.

doi:10.1109/48.838986

Cited by 211 publications

(123 citation statements)

References 31 publications

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“…Besides, every element of these matrices may split into a pair of relatively close values regarding positive and negative velocities for each DoF, and may yet vary about the nominal values (Caccia et al, 2000;Lewandowski, 2004). The factors affecting M naturally affect C(ν).…”

Section: Control Plant Modelmentioning

confidence: 99%

“…The nominal matrices C(ν) and D Q may differ from their actual counterparts, as C(ν) and D Q have fixed entries, whereas both actual matrices may vary during operation, see Section 2. The nominal matrices often have diagonal structures, because it is difficult to obtain accurate estimates of their nondiagonal elements (Caccia et al, 2000;Fossen, 2011;Lewandowski, 2004;Sørensen, 2013). The vector g may also slightly differ from the actual g. The vector u LIN represents the best endeavour towards linearising the dynamics of the CPM.…”

Section: Linearisation Of the Dynamics Of The Cpmmentioning

confidence: 99%

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Trajectory Tracking Motion Control System for Observation Class ROVs

Fernandes

Sørensen

Donha

2013

IFAC Proceedings Volumes

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This paper proposes a MCS (Motion Control System) with trajectory tracking capability for observation class ROVs (Remotely Operated Vehicle) used to carry out automated high-resolution image capturing missions, e.g. inspections, mappings, and surveys. The trajectory tracking capability is a key feature to enable the end users of the ROV technology to acquire sequential high-quality images at proper pace to construct consistent representations of the objects or of the environments of interest. Four degrees-of-freedom are controlled -surge, sway, heave, and yaw. The MCS consists of an output feedback control system that is composed of a high-gain state observer and a MIMO (Multiple-Input-Multiple-Output) PID (ProportionalIntegral-Derivative) controller, that works aided by reference feedforward. Plant linearisation is performed aiming at improving the tracking performance. Stability and satisfactory performance of suitable and smooth reference trajectories are attained despite the presence of unmodelled dynamics, plant parameter variations, measurement noise, and environmental disturbances. Simulated results based on the model of the NTNU's ROV Minerva are presented.

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Section: Control Plant Modelmentioning

confidence: 99%

Section: Linearisation Of the Dynamics Of The Cpmmentioning

confidence: 99%

Trajectory Tracking Motion Control System for Observation Class ROVs

Fernandes

Sørensen

Donha

2013

IFAC Proceedings Volumes

View full text Add to dashboard Cite

show abstract

“…Another approach uses system identification way such as adaptive and least-square-based estimation to estimate the parameters of the ROV. It was applied to the following ROVs namely: ROV Hylas [19], ROMEO [2], Johns Hopkins University ROV (JHUROV) [20], C-SCOUT AUV [21] and VideoRay ROV [22]. The results showed the adaptive method was able to predict the ROV motion better than the least-square method.…”

Section: Introductionmentioning

confidence: 99%

“…A dynamics model of an ROV for designing the GNC [1] is required. Unfortunately, the six degrees of freedom (DoF) dynamics model of the ROV [2][3][4][5] is harder to model than the streamlined autonomous underwater vehicle (AUV) [6][7][8][9][10][11][12][13][14][15] which exists an analytical solution for the hydrodynamics parameters.…”

Section: Introductionmentioning

confidence: 99%

Experimental validation of open-frame ROV model for virtual reality simulation and control

Chin

Lin

2017

J Mar Sci Technol

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The hydrodynamic damping and added mass of a remotely operated vehicle (ROV) are difficult to model. This paper provided an intuitive modeling and simulation approach to obtain the hydrodynamic damping and added mass coefficients of an open-frame ROV using computational fluid dynamic (CFD) approach in the preliminary design stage where extensive hydrodynamic test facilities are not available. The software MATLAB TM , STAR CCM?TM and WAMIT TM are employed to compute the hydrodynamic damping coefficients and added mass coefficients of the ROV for control system design and virtual reality. Experimental validation for the heave and yaw responses in a water tank shows a close relation and insight to the simulation results for subsequent control system design.

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“…Often, smaller vehicles are modelled, and the common approach is to apply least-squares estimation of a coupled or uncoupled model to a pre-recorded data set. Identification of a decoupled model for an ROV is found in [13]. Comparison of ordinary least-squares (OLS) and total least-squares for a coupled 3D parameter identification is reported in [14].…”

Section: Introductionmentioning

confidence: 99%

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

Mišković

Nađ

Vukić

2016

Adaptive Control & Signal

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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.

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Modeling and identification of open-frame variable configuration unmanned underwater vehicles

Cited by 211 publications

References 31 publications

Trajectory Tracking Motion Control System for Observation Class ROVs

Trajectory Tracking Motion Control System for Observation Class ROVs

Experimental validation of open-frame ROV model for virtual reality simulation and control

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

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