Identification of Pitch Dynamics of An Autonomous Underwater Vehicle Using Sensor Fusion

Abtahi, Seid Farhad; Alishahi, Mohammad Mehdi; Yazdi, Ehsan Azadi

doi:10.1007/s13344-019-0054-0

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…The dynamic model of an AUV is of high nonlinear, fast time-varying, and strong coupling [1]. It is difficult to obtain the hydrodynamic coefficient of underwater vehicles accurately, and it is hard to ensure the accuracy of its mathematical model [2]. Meanwhile, the underwater vehicle will be strongly disturbed by the sea currents and waves during its steering [3].…”

Section: Introductionmentioning

confidence: 99%

Research on L1 Adaptive Control of Autonomous Underwater Vehicles with X-Rudder

Yuan,

She,

Zhang

et al. 2023

JMSE

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This study focuses on addressing the the coupling problem of the vertical and horizontal plane of an autonomous underwater vehicle (AUV) with an X-rudder. To guarantee the steering performance of the AUV, a depth and course control algorithm based on an L1 adaptive control algorithm (L1AC) is proposed. Firstly, the linear quadratic regulator (LQR) is designed on the basis of an AUV vertical and horizontal linear model, and the system has optimal control quality for both of the nominal vertical and horizontal models. Secondly, the nonlinear state predictor, disturbance estimator, and adaptive controller are designed separately, which is used for the compensation of the uncertainty and disturbance of the AUV dynamic. Finally, the rudder angle allocation algorithm is designed to realize the rudder angle command of the cross rudder to the X-rudder. The simulation and experiment results show that the adaptive control algorithm proposed in this paper can effectively control AUV to complete high-performance course and depth control. Based on the experiments, the prospects and shortcomings of the L1AC, LQR and MRAC algorithm in AUV control are compared and analyzed.

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Section: Introductionmentioning

confidence: 99%

Research on L1 Adaptive Control of Autonomous Underwater Vehicles with X-Rudder

Yuan,

She,

Zhang

et al. 2023

JMSE

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“…In [15], an AUV is modeled by combining least squares and support vector machine, and the main sensitive parameters of AUV are analyzed by combining the method of sensitivity analysis. In [16], the authors synthesized inertial measurement and pitot tube measurement data to realize AUV model identification.…”

Section: Introductionmentioning

confidence: 99%

Modeling of Autonomous Underwater Vehicles with Multi-Propellers Based on Maximum Likelihood Method

Min

Gao

2020

JMSE

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The hydrodynamic characteristics of multi-propeller autonomous underwater vehicles (AUV) is usually complicated and it is difficult to obtain an accurate mathematical model. A modeling method based on CFD calculation and maximum likelihood identification algorithm is proposed for this problem. Firstly, rough hydrodynamic parameters of AUV hull are obtained by CFD calculation. Secondly, on the basis of rough parameters, a maximum likelihood identification algorithm is proposed to adjust the parameters and improve the model precision. Besides, the method to improve the convergence of identification algorithm is analyzed by considering the characteristics of AUV model structure. Finally, the identification algorithm and identification results were validated with experimental data. It was found that this method has good convergence and adaptability. In particular, the identification results of turning force and torque parameters are highly consistent in different identification experiments, which indicates that this method can well extract the maneuvering characteristics of AUVs, thus contributing to the controller design of AUVs. The research of this paper has potential application for the modeling and control of multi-propeller AUVs.

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Direction of Arrival Estimation of Acoustic Sources with Unmanned Underwater Vehicle Swarm via Matrix Completion

Huang

Zhang

et al. 2022

Remote Sensing

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With the rapid development of vibration and noise reduction technologies, underwater target detection is facing great challenges. Particularly, the task of high-resolution direction of arrival (DOA) estimation with sonar array is becoming more and more tough. In recent years, unmanned underwater vehicles (UUVs) have been developed considerably, with the improvements of target localization performance in terms of adaptability, detection range, operation efficiency, and anti-interference ability. Nevertheless, in general, the size of UUV is small such that current passive sonar systems usually have relatively limited localization accuracy, detection distance, and environmental robustness in complex ocean noise. This motivates us to present a new approach to construct a large-aperture virtual array with multiple small-aperture arrays of unmanned underwater vehicle swarm (UUVS) which consists of multiple UUVs in this paper. However, for the UUVS array, the received data could suffer from unobserved and corrupted samples. This makes it challenging to analyze and process large-aperture array data. Towards this end, the matrix completion technique is employed to recover the unobserved and corrupted data for virtual array construction based on the low rank property of array data matrix. The recovered matrix is then exploited for underwater target bearing estimation using the traditional DOA estimation approach. Numerical results verify that the proposed method is capable of detecting underwater targets with high precision and resolution.

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Identification of Pitch Dynamics of An Autonomous Underwater Vehicle Using Sensor Fusion

Cited by 3 publications

References 15 publications

Research on L1 Adaptive Control of Autonomous Underwater Vehicles with X-Rudder

Research on L1 Adaptive Control of Autonomous Underwater Vehicles with X-Rudder

Modeling of Autonomous Underwater Vehicles with Multi-Propellers Based on Maximum Likelihood Method

Direction of Arrival Estimation of Acoustic Sources with Unmanned Underwater Vehicle Swarm via Matrix Completion

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