Identification of Non Linear Models of Unmanned Underwater Vehicles: Comparison Between Two Identification Methods

El-Fakdi, Andrés; Tiano, A.; Ridao, Pere; Batlle, Joan

doi:10.1016/s1474-6670(17)37776-5

Cited by 5 publications

(16 citation statements)

References 1 publication

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“…Therefore, the identification method is decided to be based on the minimization of one-step prediction error, which is inherently expected to be more accurate. 18,19 In this study, one-step prediction error model is…”

Section: Identification Methodsmentioning

confidence: 99%

“…In El-Fakdi et al 18 and Ridao et al, 19 the one-step prediction error model is linear in unknowns and the minimization problem is solved using the least square method. However, the equation of one-step prediction error of this study (equation (34)) is nonlinear in unknown parameters.…”

Section: Identification Methodsmentioning

confidence: 99%

“…In Petrich et al, 17 pitch dynamics of Virginia Tech 475 AUV was identified. In El-Fakdi et al, 18 two methods of identification were implemented and their results were compared for an UUV. The first method was based on the minimization of acceleration prediction error while the other one was based on the minimization of one-step velocity prediction error.…”

Section: Introductionmentioning

confidence: 99%

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Identification of roll dynamics of an autonomous underwater vehicle

Abtahi

Alishahi

Yazdi

2018

Proceedings of the Institution of Mechanical Engineers, Part M:

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This article aims to identify the roll channel parameters of an autonomous underwater vehicle. These parameters include hydrodynamic coefficients, motor torque, eccentricity, misalignments and mounting imperfections. In the proposed method, an approximation of the hydrodynamic coefficients is made at first via semi-empirical methods. In the next step, a proportional–integral–derivative controller is designed with respect to the approximated coefficients. Since the approximations can be very uncertain, the robustness of stability and performance of the proportional–integral–derivative controller is evaluated throughout µ-analysis. Finally, the unknown parameters are identified using the recorded data of on-board sensors during motion of the vehicle. The identification is based on minimization of the one-step prediction error. The minimization problem is nonlinear in unknown parameters, and particle swarm optimization is used to find an optimal solution. The performance of the proposed method is exhibited through a 6-degrees-of-freedom simulation of an autonomous underwater vehicle.

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Section: Identification Methodsmentioning

confidence: 99%

Section: Identification Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identification of roll dynamics of an autonomous underwater vehicle

Abtahi

Alishahi

Yazdi

2018

Proceedings of the Institution of Mechanical Engineers, Part M:

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“…In order to estimate the hydrodynamic parameters of a prescribed underwater vehicle, different experimental methodologies have been reported in the literature [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ], yet, the majority of them require the implementation of top-technological expensive gadgets and tools, which makes the design task difficult for those researchers whose budget and facilities are an issue to overcome. In this regard, the implementation of Computed Fluid Dynamics (CFD) software and other computational resources has emerged as an alternative to conceive the vehicles at the first stages of research.…”

Section: Introductionmentioning

confidence: 99%

Mini-AUV Hydrodynamic Parameters Identification via CFD Simulations and Their Application on Control Performance Evaluation

Castillo-Zamora

Camarillo‐Gómez

Pérez-Soto

et al. 2021

Sensors

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This manuscript presents a fully detailed methodology in order to identify the hydrodynamic parameters of a mini autonomous underwater vehicle (mini-AUV) and evaluate its performance using different controllers. The methodology consists of close-to-reality simulation using a Computed Fluid Dynamics (CFD) module of the ANSYS™ Workbench software, the processing of the data, obtained by simulation, with a set of Savistky–Golay filters; and, the application of the Least Square Method in order to estimate the hydrodynamic parameters of the mini-AUV. Finally, these parameters are considered to design the three different controllers that are based on the robot manipulators theory. Numerical simulations are carried out to evaluate the performance of the controllers.

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“…In Chou et al, 23 a method based on laser line scanning was developed to identify the dynamics of an AUV. In El-Fakdi et al, 24 using URIS UUV, two identification methods were applied and their results were analyzed and compared. The first method aims to minimize the error of acceleration prediction, while the second method aims to minimize the error of one step velocity prediction.…”

Section: Introductionmentioning

confidence: 99%

Developing data fusion and recursive estimation methods for online identification of dive plane dynamics of an autonomous underwater vehicle

Abtahi

Alishahi

Yazdi

2019

Proceedings of the Institution of Mechanical Engineers, Part M:

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The purpose of this article is to develop an online method to identify the hydrodynamic coefficients of pitch plane of an autonomous underwater vehicle. To obtain necessary data for the identification, the dive plane dynamics should be excited through diving maneuvers. Hence, a controller is needed whose performance and stability are appropriate. To design such a controller, first, hydrodynamic coefficients are approximated using semi-empirical methods. Based on these approximated coefficients, a classic controller is designed at the next step. Since the estimation of these coefficients is uncertain, µ-analysis is employed to verify the robustness of stability and performance of the controller. Using the verified robust controller, some oscillating maneuvers are carried out that excite the dive plane dynamics. Using sensor fusion and unscented Kalman filter, smooth and high-rate data of depth is provided for the depth controller. A recursive identification algorithm is developed to identify the hydrodynamic coefficients of heave and pitch motions. It turns out that some inputs required by the identification are not measured directly by the sensors. But the devised fusion algorithm is able to provide the necessary data for identification. Finally, using the identified coefficients and employing pole placement method, a new controller with better performance is synthesized online. To evaluate the performance of the identification and fusion algorithms, a 6-degree-of-freedom simulation of an autonomous underwater vehicle is carried out.

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Identification of Non Linear Models of Unmanned Underwater Vehicles: Comparison Between Two Identification Methods

Cited by 5 publications

References 1 publication

Identification of roll dynamics of an autonomous underwater vehicle

Identification of roll dynamics of an autonomous underwater vehicle

Mini-AUV Hydrodynamic Parameters Identification via CFD Simulations and Their Application on Control Performance Evaluation

Developing data fusion and recursive estimation methods for online identification of dive plane dynamics of an autonomous underwater vehicle

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