Thruster Fault Diagnostics and Fault Tolerant Control for Autonomous Underwater Vehicle with Ocean Currents

Tian, Qunhong; Wang, Tao; Liu, Bing; Ran, Guangtao

doi:10.3390/machines10070582

Cited by 14 publications

(9 citation statements)

References 30 publications

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“…Ref. [19] addressed the aforementioned problem by introducing a parameterized approach for designing two dynamic compensators, offering a solution to the reliable stabilization problem. Ref.…”

Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Control of Autonomous Underwater Vehicle Actuators Based on Takagi and Sugeno Fuzzy and Pseudo-Inverse Quadratic Programming under Constraints

Zhang,

Wu,

Zhou

et al. 2024

Sensors

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Autonomous Underwater Vehicles (AUVs) play a significant role in ocean-related research fields as tools for human exploration and the development of marine resources. However, the uncertainty of the underwater environment and the complexity of underwater motion pose significant challenges to the fault-tolerant control of AUV actuators. This paper presents a fault-tolerant control strategy for AUV actuators based onTakagi and Sugeno (T-S) fuzzy logic and pseudo-inverse quadratic programming under control constraints, aimed at addressing potential actuator faults. Firstly, considering the steady-state performance and dynamic performance of the control system, a T-S fuzzy controller is designed. Next, based on the redundant configuration of the actuators, the propulsion system is normalized, and the fault-tolerant control of AUV actuators is achieved using the pseudo-inverse method under thrust allocation. When control is constrained, a quadratic programming approach is used to compensate for the input control quantity. Finally, the effectiveness of the fuzzy control and fault-tolerant control allocation methods studied in this paper is validated through mathematical simulation. The experimental results indicate that in various fault scenarios, the pseudo-inverse combined with a nonlinear quadratic programming algorithm can compensate for the missing control inputs due to control constraints, ensuring the normal thrust of AUV actuators and achieving the expected fault-tolerant effect.

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Section: Introductionmentioning

confidence: 99%

Fault-Tolerant Control of Autonomous Underwater Vehicle Actuators Based on Takagi and Sugeno Fuzzy and Pseudo-Inverse Quadratic Programming under Constraints

Zhang,

Wu,

Zhou

et al. 2024

Sensors

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show abstract

“…How to extract fault features from this complex signal has always been the hot point problem of underwater thruster fault diagnosis. 10 Common fault diagnosis methods include the state observer, 11,12 hidden Markov model, 13,14 wavelet time-frequency analysis, 15 D-S evidence theory, 16 support vector machine, 17 and neural network. 18,19 The application of deep learning technology, such as neural network, to intelligent fault diagnosis of underwater thrusters has attracted the attention of researchers.…”

Section: Introductionmentioning

confidence: 99%

“…Because the imbalanced fault signal of underwater thruster is affected by load and fluid medium, and it has strong non‐stationary and low signal‐to‐noise ratio. How to extract fault features from this complex signal has always been the hot point problem of underwater thruster fault diagnosis 10 . Common fault diagnosis methods include the state observer, 11,12 hidden Markov model, 13,14 wavelet time‐frequency analysis, 15 D‐S evidence theory, 16 support vector machine, 17 and neural network 18,19 .…”

Section: Introductionmentioning

confidence: 99%

Correlation analysis and fault detection of current and speed signals for underwater thruster entanglement

Zhang¹,

Geng²,

Wang³

2023

Adv Control Appl

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The fault detection with a single sensor cannot comprehensively use the multi‐sensor correlation information of underwater thruster. To solve the issue that in the case of entanglement, a kind of fault diagnosis method of underwater thruster based on the combination of current and rotational speed signal correlation analysis and support vector machine is proposed. Firstly, the collected current and speed signals of underwater thrusters under different states are normalized, the variable sampling data points is adopted to refine the time of fault occurrence; Secondly, the cross correlation and autocorrelation coefficients of normalized current and speed signals are calculated based the variable sampling data points number and the correlation matrix is formed at different sampling time based on the cross correlation and autocorrelation coefficients. Finally, the support vector machine was applied to diagnose whether or not the fault produces and the time of fault occurrence with the correlation coefficients sequence. To verify the effectiveness of the proposed method, the fault simulation platform and data acquisition software are designed, the fault data in the case of thruster with entanglement are collected in time to test the proposed method. The results show that compared with the fault diagnosis method using only one speed or current signal, the proposed analytical method based on the correlation coefficients can fully extract the correlation information of underwater thruster fault, and the time extraction of fault occurrence is more sufficient, effectively improving the accuracy of underwater thruster fault diagnosis.

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“…The FDI method proposed by Talebi et al [8] is used to detect, isolate, and identify the severity of actuator failures in the presence of disturbances and fault uncertainty in the model. To improve the performances of thruster fault diagnosis, Tian et al [9] proposed a possible fuzzy C-means algorithm for thruster fault classification. Omerdic et al [10] proposed a novel thruster fault diagnosis and adaptation system for open-frame underwater vehicles.…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method for the Autonomous Underwater Vehicle via Meta-Self-Attention Multi-Scale CNN

Chen

Wang

et al. 2023

JMSE

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Autonomous underwater vehicles (AUVs) are an important equipment for ocean investigation. Actuator fault diagnosis is essential to ensure the sailing safety of AUVs. However, the lack of failure data for training due to unknown ocean environments and unpredictable failure occurrences is challenging for fault diagnosis. In this paper, a meta-self-attention multi-scale convolution neural network (MSAMS–CNN) is proposed for the actuator fault diagnosis of AUVs. Specifically, a two-dimensional spectrogram of the vibration signals obtained by a vibration sensor is used as the neural network’s inputs. The diagnostic model is fitted by executing a subtask-based gradient optimization procedure to generate more general degradation knowledge. A self-attentive multi-scale feature extraction approach is used to utilize both global and local features for learning important parameters autonomously. In addition, a meta-learning method is utilized to train the diagnostic model without a large amount of labeled data, which enhances the generalization ability and allows for cross-task training. Experimental studies with real AUV data collected by vibration sensors are conducted to validate the effectiveness of the MSAMS–CNN. The results show that the proposed method can diagnose the rudder and thruster faults of AUVs in the cases of few-shot diagnosis.

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Thruster Fault Diagnostics and Fault Tolerant Control for Autonomous Underwater Vehicle with Ocean Currents

Cited by 14 publications

References 30 publications

Fault-Tolerant Control of Autonomous Underwater Vehicle Actuators Based on Takagi and Sugeno Fuzzy and Pseudo-Inverse Quadratic Programming under Constraints

Fault-Tolerant Control of Autonomous Underwater Vehicle Actuators Based on Takagi and Sugeno Fuzzy and Pseudo-Inverse Quadratic Programming under Constraints

Correlation analysis and fault detection of current and speed signals for underwater thruster entanglement

A Fault Diagnosis Method for the Autonomous Underwater Vehicle via Meta-Self-Attention Multi-Scale CNN

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