Identification of roll dynamics of an autonomous underwater vehicle

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

doi:10.1177/1475090218810248

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…In this section, the parameter identification method is designed for the system equations of the previous subsection. There are more methods for parameter identification; for example, the least squares method and the maximum likelihood method [42]. In contrast to these two methods, neural networks with nonlinear mapping capability are selected to identify the parameter of this system.…”

Section: Rnn Structurementioning

confidence: 99%

Data-Driven Fault Detection of AUV Rudder System: A Mixture Model Approach

Zhang

Zhang²,

Yan

et al. 2023

Machines

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Based on data-driven and mixed models, this study proposes a fault detection method for autonomous underwater vehicle (AUV) rudder systems. The proposed method can effectively detect faults in the absence of angle feedback from the rudder. Considering the parameter uncertainty of the AUV motion model resulting from the dynamics analysis method, we present a parameter identification method based on the recurrent neural network (RNN). Prior to identification, singular value decomposition (SVD) was chosen to denoise the original sensor data as the data pretreatment step. The proposed method provides more accurate predictions than recursive least squares (RLSs) and a single RNN. In order to reduce the influence of sensor parameter errors and prediction model errors, the adaptive threshold is mentioned as a method for analyzing prediction errors. In the meantime, the results of the threshold analysis were combined with the qualitative force analysis to determine the rudder system’s fault diagnosis and location. Experiments conducted at sea demonstrate the feasibility and effectiveness of the proposed method.

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Section: Rnn Structurementioning

confidence: 99%

Data-Driven Fault Detection of AUV Rudder System: A Mixture Model Approach

Zhang

Zhang²,

Yan

et al. 2023

Machines

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“…2 Among other fields, research has been carried out on their operations and deployment and recovery systems, 3 as well as the system of control 4 and fault detection, 5 using predictive models for the control of this type of vessel 6 or identifying the parameters of the balancing channel, including hydrodynamic coefficients, motor torque, eccentricity, misalignments and mounting imperfections. 7 Other current research also focuses on the study of topics such as the path tracking control methodologies, 8 the structural design, 9 the study of the flow around the hull of the vehicle 10 or multi-objective optimization of the design of the submarine’s hull shape, 11 with the aim of reducing the energy consumption, the construction of testing scenarios 12 and the development of new technologies, such as the use of multi-sonar controllers. 13…”

Section: Introductionmentioning

confidence: 99%

Dimensional control of cylindrical structures for unmanned underwater vehicles: Comparative study of the most advanced techniques

Mascaraque‐Ramírez

Martínez-Botella

2020

Proceedings of the Institution of Mechanical Engineers, Part M:

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Unmanned underwater vehicles require a hull resistant to immersion and, therefore, to hydrostatic pressure, which places high demands on its design, manufacture and inspection, where dimensional control is a fundamental part of the verification of the shapes of the structure. In this research, a comparison is made of the dimensional control techniques currently used for the naval sector and their viability in the field of unmanned underwater vehicles. A cylindrical body steel model will be measured, with the characteristic dimensions of the unmanned underwater vehicles, executing tests using three measurement techniques, such as the optical measurement of surface points, the surface probing technology and the surface laser scanner with large volume of points. Experimental data will be provided in terms of mean diameter of the sections that make up the hull, defect of circularity and position of the optimum centre, which will lead to the development of a guide of good practices for selecting and using each technique depending on the requirements and demands of the unmanned underwater vehicle under study.

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“…Using LS method, in Petrich et al, 18 dynamics of the dive plane of Virginia Tech 475 AUV was identified. In Abtahi et al, 19 an offline method was developed to identify the roll dynamics of an AUV. In Yan et al, 20 a recursive subspace identification method was developed and simulated for an AUV.…”

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 roll dynamics of an autonomous underwater vehicle

Cited by 3 publications

References 22 publications

Data-Driven Fault Detection of AUV Rudder System: A Mixture Model Approach

Data-Driven Fault Detection of AUV Rudder System: A Mixture Model Approach

Dimensional control of cylindrical structures for unmanned underwater vehicles: Comparative study of the most advanced techniques

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

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