Identification of hydrodynamic coefficients in ship maneuvering equations of motion by Estimation-Before-Modeling technique

Yoon, Hyeon Kyu; Rhee, Key-Pyo

doi:10.1016/s0029-8018(03)00106-9

Cited by 126 publications

(46 citation statements)

References 5 publications

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“…Time domain identification methods are also used for LTI systems [Jategaonkar, 2006, Hamel and Jategaonkar, 1996, Klein, 1989, and common applications include the determination of aerodynamic or hydrodynamic coefficients [Sri-Jayantha and Stengel, 1988, Stalford, 1981, Yoon et al, 2004, Yoon and Rhee, 2003]. Identifying continuous time [Rao and Unbehauen, 2006] models requires the computation of signal derivatives while identifying discrete time models does not.…”

Section: Parameter Identificationmentioning

confidence: 99%

“…Parameter identification methods applied to surface vessels include frequency domain approaches [Selvam et al, 2005] and time domain approaches [Källström andÅström, 1981,Åström and Källström, 1976, Muske et al, 2008, Hayes, 1971, Mišković et al, 2011. Optimization of parameters is done using hybrid-extended Kalman filtering [Yoon and Rhee, 2003, Yoon et al, 2004, Hayes, 1971, Fossen et al, 1996, recursive least squares [Nguyen, 2008], and artificial neural network (black box) methods [Rajesh and Bhattacharyya, 2008]. Note that all identification methods require parameter optimization, though the specific algorithms are often not explicitly stated.…”

Section: Parameter Identificationmentioning

confidence: 99%

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Modeling, Identification, and Control of an Unmanned Surface Vehicle

Sonnenburg

Woolsey

2013

Journal of Field Robotics

139

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This dissertation addresses the modeling, identification, and control of an automated planing vessel. To provide motion models for trajectory generation and to enable model-based control design for trajectory tracking, several experimentally identified models are compared over a wide range of speed and planing conditions for the Virginia Tech Ribcraft Unmanned Surface Vehicle. The modeling and identification objective is to determine a model which is sufficiently rich to enable effective model-based control design and trajectory optimization, sufficiently simple to allow parameter identification, and sufficiently general to describe a variety of hull forms and actuator configurations. Beginning with a 6 degree of freedom nonlinear dynamic model, several linear steering and speed models are obtained as well as a thruster model.The Ribcraft USV tracks trajectories generated with the selected maneuvering models by using a backstepping trajectory controller. A PD cascade trajectory control law is also developed and the performance of the two controllers is compared using aggressive trajectories. The backstepping control law compares favorably to the PD cascade controller. The backstepping control law is then further modified to account for nonlinear sternward dynamics and for a constant or slowly varying fluid flow. ACKNOWLEDGMENTSThis dissertation is the product of the culmination of years of research and field experimentation that required the collective abilities only found in a group of skilled researchers. There are many people that guided me through the higher level decision-making and advising process as well as the more laborious experimentation and implementation efforts. Without these people I would not have been able to finish a dissertation even approaching this quality.

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Section: Parameter Identificationmentioning

confidence: 99%

Section: Parameter Identificationmentioning

confidence: 99%

Modeling, Identification, and Control of an Unmanned Surface Vehicle

Sonnenburg

Woolsey

2013

Journal of Field Robotics

139

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“…Este requisito es también de suma importancia en aplicaciones de control de movimiento en el que, si el modelo matemático utilizado para el diseño de control no es exacto cuando se consideran las condiciones de funcionamiento del vehículo, o si existen perturbaciones externas, es difícil ajustar el controlador para un buen comportamiento del vehículo. A este respecto, se encuentran en la literatura un importante número de contribuciones en lo que se refiere al ajuste del modelo de maniobra y también a la exactitud del mismo cuando se aplican mínimos cuadrados ordinarios (MCO) con vehículos de superficie (Shields and Hodder, 1982), (Suleiman, 2000), (Yoon and Rhee, 2003), (Oltmann, 2003), (Mahfouz, 2004), (Yoon et al, 2007), (Revestido Herrero and Velasco, 2012). De la misma manera, en lo que se refiere a vehícu-los subacuáticos se encuentran referencias (Alessandri et al, 1998), (Caccia et al, 2000), (Smallwood and Whitcomb, 2003), (Tiano, 2004), (Hegrenaes et al, 2007), (Vervoort, 2009), (Miskovic and Vukic, 2011), (Gibson et al, 2015) donde también se hace hincapié en el ajuste de los datos y en la exactitud del modelo cuando se aplican MCO.…”

Section: Introductionunclassified

Estimación Eficiente de Parámetros y Control en Base a un Sistema de Guía LOS Modificado de un Vehículo Subacuático

Herrero

Velasco

Vega

et al. 2017

Rev. iberoam. autom. inform. ind.

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ResumenEn este trabajo, se propone una metodología para la mejora de la eficiencia en la estimación de parámetros de un modelo de maniobra no lineal de un vehículo subacuático no tripulado con forma de torpedo. Para este cometido, se dispone de datos de diferentes ensayos, llevados a cabo con el citado vehículo en las instalaciones del Canal de Experiencias Hidrodinámicas del Pardo, Madrid. En la metodología propuesta, se tiene en cuenta los siguientes aspectos para mejorar la eficiencia en la estimación de los parámetros: selección del periodo de muestreo, suavizado de los datos adquiridos en los ensayos considerando un compromiso entre varianza y sesgo del filtro suavizador a aplicar, análisis del modelo de regresión lineal clásico planteado en cada ensayo, desde el punto de vista estadístico para la estimación de los parámetros. Las mejora de la eficiencia se verifica mediante métodos gráficos y estadísticos. Además, se propone una modificación del método line-of-sight (LOS) convencional que proporciona resultados satisfactorios en presencia de corrientes oceánicas mediante la realización de un procedimiento sencillo.Palabras Clave: Estimación de parámetros, mínimos cuadrados ordinarios, mínimos cuadrados generales, modelo de maniobra no lineal, LOS. Efficient Parameter Estimation and Control Based on a Modified LOS Guidance System of an Underwater Vehicle AbstractIn this work, a methodology is proposed for the improvement of the parameter estimation efficiency of a non-linear manoeuvring model of a torpedo shaped unmanned underwater vehicle. For this purpose, data from different tests, were carried out with the aforementioned vehicle at the facilities of the Canal de Experiencias Hidrodinámicas del Pardo, Madrid. In the proposed methodology, the following aspects are taken into account in order to improve the parameter estimation efficiency: selection of the sampling period, smoothing of the data acquired in the tests considering a compromise between variance and bias of the smoothing filter to be applied, analysis of the classical linear regression model proposed in each trial, from the statistical point of view for the estimation of the parameters. Improvements in efficiency are verified by graphical and statistical methods. In addition, a modification of the conventional LOS method is proposed which provides satisfactory results in the presence of ocean currents by performing a simple procedure.

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“…A representative observer method is the Kalman filter, which has been widely used in the estimation of hydrodynamic coefficients and state variables. Hwang [2], Kim [3], and Yoon [4] estimated the maneuvering coefficients of a ship, and identified the dynamic system of a maneuvering ship, using an extended Kalman filtering technique.…”

Section: Introductionmentioning

confidence: 99%

Controller design for an autonomous underwater vehicle using nonlinear observers

Negahdaripour¹,

Cho²,

Kim³

2011

International Journal of Ocean System Engineering

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The depth and heading control of an autonomous underwater vehicle (AUV) are considered to follow the predetermined depth and heading angle. The proposed control algorithm was based on a sliding mode control, using estimated hydrodynamic coefficients. The hydrodynamic coefficients were estimated employing conventional nonlinear observer techniques, such as sliding mode observer and extended Kalman filter. Using the estimated coefficients, a sliding mode controller was constructed for a combined diving and steering maneuver. The simulated results of the proposed control system were compared with those of a control system that employed true coefficients. This paper demonstrated the proposed control system, and discusses the mechanisms that make the system stable and accurately follow the desired depth and heading angle in the presence of parameter uncertainty.

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Identification of hydrodynamic coefficients in ship maneuvering equations of motion by Estimation-Before-Modeling technique

Cited by 126 publications

References 5 publications

Modeling, Identification, and Control of an Unmanned Surface Vehicle

Modeling, Identification, and Control of an Unmanned Surface Vehicle

Estimación Eficiente de Parámetros y Control en Base a un Sistema de Guía LOS Modificado de un Vehículo Subacuático

Controller design for an autonomous underwater vehicle using nonlinear observers

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