Identification of hydrodynamic coefficients of AUV in the presence of measurement biases

Dinc, Mustafa; Hajiyev, Chingiz

doi:10.1177/14750902211057478

Cited by 2 publications

(2 citation statements)

References 21 publications

(31 reference statements)

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“…Least squares parameter identification was proposed by Dinç to estimate hydrodynamic coefficients for autonomous underwater vehicles in the presence of measurement biases [9], while this present manuscript illustrates application of such techniques to estimation of actuator motor parameters. Gutnik, et al illustrated how the efficacy of such approaches manifest in operational capabilities for autonomous, near-seabed visual imaging missions [10], particularly as it pertains to thrust allocation for path following.…”

Section: Introductionmentioning

confidence: 93%

Artificial Intelligence Enhanced UUV Actuator Control

Wang¹,

Sands²

2023

Preprint

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This manuscript compares deterministic artificial intelligence to model following control applied to DC motor control, including evaluation of the threshold computation rate to let unmanned underwater vehicles correctly follow the challenging discontinuous square wave command signal. The approaches presented in the main text are validated in MATLAB®, where the motor process is discretized at multiple step sizes, which is inversely proportional to the computation rate. The performance is evaluated by the error mean and standard deviation. With a large step size, discrete deterministic artificial intelligence shows a larger error mean than the model-following self-turning regulator approach (benchmark selection). However, the performance gets optimized with the step size decreased. The error mean is close to the continuous deterministic artificial intelligence when the step size is reduced to 0.2 seconds, which means that the computation rate and the sampling period restrict discrete deterministic artificial intelligence. In that case, the continuous deterministic artificial intelligence is the most feasible and reliable selection for future applications on unmanned underwater vehicles since it is superior to all the approaches with multiple computation rates.

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Section: Introductionmentioning

confidence: 93%

Artificial Intelligence Enhanced UUV Actuator Control

Wang¹,

Sands²

2023

Preprint

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“…Additionally, Xiao [16] and Zhao [17] employed the maximum likelihood estimation algorithm to identify a simplified model of underwater vehicles and their hydrodynamic coefficients in different directions. Zhang et al [18] and Mustafa et al [19] utilized the least squares method to identify partial hydrodynamic coefficients of surface ships and underwater vehicles, respectively. Xue et al [20] used the Bayesian method to identify the four-degree-of-freedom hydrodynamic coefficients of surface ships through simulated motion data.…”

Section: Introductionmentioning

confidence: 99%

Identification of Hydrodynamic Coefficients of the SUBOFF Submarine Using the Bayesian Ridge Regression Model

Xiang,

Ou,

Chen

et al. 2023

Applied Sciences

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Maneuverability is one of the submarine’s most important features, and is closely related to hydrodynamic coefficients. The submarine standard motion equation is the most commonly used hydrodynamic mathematical model, estimating more than 100 hydrodynamic coefficients. This paper applies the Bayesian ridge regression model to identify a submarine’s hydrodynamic coefficients. Specifically, the proposed approach combines the URANS equation with a six-degree-of-freedom motion model and a body force propeller model, and uses overset grid technology to simulate the underwater six-degree-of-freedom navigation motion of the SUBOFF submarine. The submarine’s required hydrodynamic coefficients are obtained by collecting relevant velocity and angular velocity data and applying the Bayesian ridge regression model for data identification. Meanwhile, CFD simulation of the restraint model test for the SUBOFF submarine is conducted to obtain the related hydrodynamic coefficients. Through comparative experiments, we validate that the proposed Bayesian ridge regression model identification method is effective and reliable; most of the errors in the hydrodynamic coefficients were within 10%, with a maximum error of −43.26%, providing more comprehensive and timely hydrodynamic coefficients than traditional CFD restraint model tests. Furthermore, the hydrodynamic coefficient identification results were used to invert the submarine’s spatial motion, and we demonstrated that the resulting trajectory, velocity, and angular velocity curves all fit well.

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Identification of hydrodynamic coefficients of AUV in the presence of measurement biases

Cited by 2 publications

References 21 publications

Artificial Intelligence Enhanced UUV Actuator Control

Artificial Intelligence Enhanced UUV Actuator Control

Identification of Hydrodynamic Coefficients of the SUBOFF Submarine Using the Bayesian Ridge Regression Model

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