Auv Identification by Use of Self-Oscillations

Mišković, Nikola; Vukić, Zoran; Barisic, Matko; Soucacos, P.P.

doi:10.3182/20070919-3-hr-3904.00033

Cited by 15 publications

(17 citation statements)

References 13 publications

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“…Identification by use of self-oscillations can also be used on a class of nonlinear systems as it will be demonstrated below, see also [5] and [6]. For equation…”

Section: Identification By Use Of Self-oscillations (I-so)mentioning

confidence: 98%

“…Since then, the method has been applied in process industry for tuning controllers. First application of the method in marine control was reported in [5], [6] and [12], where it was applied for identification of an underwater vehicle dynamic model. Since then, it has been applied for heading controller tuning of marine surface vehicles, see [7] for details.…”

Section: Identification By Use Of Self-oscillations (I-so)mentioning

confidence: 99%

“…The main assumption in the self-oscillation analysis is that the process filters out higher harmonics effectively -this is the case for inertial systems, see [13]. A detailed analysis of errors that are introduced in I-SO method and methods on how to minimize them can be found in [5]. On the other hand, the I-SO method found it's application in tuning marine vehicles' controllers due to the fact that it is easily implementable, feasible in field conditions and time conservative.…”

Section: Identification By Use Of Self-oscillations (I-so)mentioning

confidence: 99%

“…This paper will demonstrate tuning Kalman filter parameters (dynamic model parameters) based on self-oscillation experiments. In [5], [6] these experiments have been used for tuning low-level controllers for underwater vehicles without using Kalman filter estimates. The method has also been applied on autonomous catamaran Charlie (heading and line following controllers) what is reported in [7].…”

Section: Introductionmentioning

confidence: 99%

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Distance keeping for underwater vehicles - tuning Kalman filters using self-oscillations

et al. 2009

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This paper describes the use of Kalman filtering in distance keeping for underwater vehicles. The vision-based distance keeping module has been mounted on a micro-ROV equipped with a camera. Distance from a plane-like obstacle is determined on the basis of the laser dot projections within the frame. Since these measurements are not reliable a Kalman filter is designed -unknown dynamic model parameters are determined using the self-oscillation experiments which prove to be simple and time preserving. Finally, an I-PD controller is designed based on the Kalman filter estimates. The main task of the controller is to keep the vehicle perpendicular to the surface at a desired distance.

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“…Identification by use of self-oscillations can also be used on a class of nonlinear systems as it will be demonstrated below, see also [5] and [6]. For equation…”

Section: Identification By Use Of Self-oscillations (I-so)mentioning

confidence: 98%

Section: Identification By Use Of Self-oscillations (I-so)mentioning

confidence: 99%

Section: Identification By Use Of Self-oscillations (I-so)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Distance keeping for underwater vehicles - tuning Kalman filters using self-oscillations

et al. 2009

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“…Contrastingly, Nikola et al adopted a self-oscillations approach in order to identify the parameters for yaw motion of an unmanned marine vehicle [4]. Self-oscillations is the behavior created within a process if it is a closed loop one and contains a non-linear element.…”

Section: Priori Artmentioning

confidence: 99%

System identification for unmanned marine vehicles using interval analysis

Das

Kumar

Pal

et al. 2014

Oceans 2014 - Taipei

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The aim in the present paper remains to describe a system identification method for unmanned marine vehicles using interval analysis in order to come up with estimation of parameter intervals instead of real values, such that the actual dynamics of the system shall remain confined within guaranteed bounds. The bounds are propagated through the system equations using such interval based parameters. The proposed method exploits guaranteed error bounds for the state variables as observed by different sensors. Unlike conventional Kalman estimators the adopted method rules out the requirement for determining covariance matrices and approximated Jacobians. Interval based variables are used in constructing interval matrices. Principle of Least Squares is used in solving the system equation involving such non-punctual (interval) matrices. In this context a sophisticated interval matrix inversion technique is employed within the least squares framework in finally determining the parameter intervals.

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Experimental Parameter Identification for an open-frame ROV: Comparison of towing tank tests and open water self-propelled tests

Lack

Jeinsch

2019

IFAC-PapersOnLine

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Auv Identification by Use of Self-Oscillations

Cited by 15 publications

References 13 publications

Distance keeping for underwater vehicles - tuning Kalman filters using self-oscillations

Distance keeping for underwater vehicles - tuning Kalman filters using self-oscillations

System identification for unmanned marine vehicles using interval analysis

Experimental Parameter Identification for an open-frame ROV: Comparison of towing tank tests and open water self-propelled tests

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