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Variational Mode Decomposition (VMD) has typically been used in weak fault feature extraction in recent years. The problem analyzed in this study is weak fault feature extraction and the enhancement of AUV thrusters based on Artificial Rabbits Optimization (ARO) and VMD. First, we introduce ARO to solve the problem of long-running times when using VMD for weak fault feature extraction. Then, we propose a VMD denoising method based on an improved ARO algorithm to address the issue of deteriorations in the fault feature extraction effect after introducing ARO. In this method, chaotic mapping and Gaussian mutation are used to improve ARO to optimize the parameters of VMD. This leads to a reduced running time and improved fault feature extraction performance. We then perform fault feature enhancement. Due to the unsatisfactory enhancement effect of traditional modified Bayes (MB) methods for weak fault features, we introduce energy operators to transform the fault signals into the energy domain for fault feature enhancement. Finally, we add differential processing to the signal to address the issue of certain fault feature values decreasing after introducing energy operators. In the end, the effectiveness of the proposed methods is verified via pool experiments on a "Beaver II" AUV prototype. [ABSTRACT FROM AUTHOR]

Weak thruster fault detection problem is investigated for Autonomous Underwater Vehicle (AUV) with being less than 10% loss of effectiveness fault in a thruster. For the weak thruster fault, the fault features are small in general, meanwhile the fault features are also difficult to be distinguished from the external disturbances including measurement noise. In this paper, a weak thruster fault detection method is developed based on Bayesian network and hidden Markov model. In the proposed method, the fault features are enhanced at first and then the fault detection is achieved based on the occurrence probability. Specifically, Bayesian network is applicable to enhance weak signals, but the prior knowledge is difficult to obtain for an AUV. To solve this problem, this paper introduces sparse decomposition into Bayesian network to enhance the weak thruster fault features. And then, a hidden Markov model is established to provide the likelihood probability between the observation sequence and the predict one. The fault detection is achieved according to comparing the probability and the threshold. Finally, pool-experimental results on an AUV are presented to verify the effectiveness of the developed fault detection method. [ABSTRACT FROM AUTHOR]

Existing thruster fault diagnosis methods for AUV (autonomous underwater vehicle) usually need sufficient labeled training data. However, it is unrealistic to get sufficient labeled training data for each working condition in practice. Based on this challenge, a transferable thruster fault diagnosis approach is proposed. In the approach, an IPSE (instantaneous power spectrum entropy) and a STNED (signal-to-noise energy difference) are added to SPWVD (smoothed pseudo Wigner-Ville distribution) to identify time and frequency boundaries of the local region in the time-frequency power spectrum caused by thruster fault, forming a TFE (time-frequency energy) method for feature extraction. In addition, the RCQFFV (relative change quantity of the fault feature value), an MSN (multiple scale normalization) and a LSP (least square prediction) are added to SVDD (support vector data description) to align distributions of fault samples, contributing a TSVDD (transferable SVDD) for classification of fault samples. The experimental results of a prototype AUV indicate that the fault feature is monotonic to the percentage of thrust loss for the proposed TFE but not for the SPWVD. The TSVDD has a higher overall classification accuracy in comparison to conventional SVDD under working conditions with no labeled training data. [ABSTRACT FROM AUTHOR]

This paper addresses the region tracking control problem for an autonomous underwater vehicle (AUV) and proposes a prediction-based region tracking control (PRTC) scheme for AUV. In the PRTC scheme, the idea of prediction is adopted to solve the problems of overshoot and high energy consumption due to the lack of consideration of the large inertia of the AUV in the traditional scheme. The PRTC scheme predicts the future position of AUV through the past time-series position of AUV and the outer boundary of the desired region, and then designs the controller depending on the predicted results. Furthermore, the relationship between the desired region and the control output of the proposed PRTC scheme is studied. It is found that its control output amplitude is susceptible to the desired region range, resulting in output saturation. Therefore, this paper proposes a control law optimization scheme considering the desired region. This optimization scheme modifies the error signal in the control law of the PRTC scheme so that it is only related to the relative position of the desired region where the AUV is located. Finally, the proposed schemes are applied on the ODIN AUV, and the simulation results verify the feasibility of the proposed schemes. [ABSTRACT FROM AUTHOR]

As the most important device of an Autonomous Underwater Vehicle (AUV), thrusters are one of the main sources of fault. If the thruster fault can be diagnosed in the early stage, it would give more time to guarantee the safety of an AUV. Fault feature extraction is the premise of fault diagnosis. The traditional feature calculation methods extract fault features from one domain. These methods work well in the case of high fault severity, but poorly in the case of weak fault severity. In addition, for weak faults, the fault features extracted by the traditional methods may not meet the monotonic relationship with fault severity and cannot be used in fault severity identification. Aiming at these problems, through experimental data analysis, this paper excludes the features that do not meet the law from the 52 selectable fault features in the time domain, frequency domain and time-frequency domain. Aiming at the problem that there is no useful feature in the frequency domain, a new feature calculation method is proposed, and the order of magnitude of the available feature is given, which provides concise and accurate information for subsequent fault feature fusion and fault severity identification. [ABSTRACT FROM AUTHOR]

This paper investigates the finite-time tracking problem for autonomous underwater vehicles (AUVs) in the presence of external disturbance and modelling uncertainty. A finite-time tracking control scheme is proposed based on an improved non-singular terminal sliding mode manifold. Firstly, in order to achieve finite-time stability and avoid singular problem, a continuous and smooth non-singular terminal sliding mode manifold is designed based on a piecewise function. And then a continuous control law is designed by indirectly estimating the bound of the general uncertainty, to compensate the external disturbance and modelling uncertainty. Based on the Lyapunov stability analysis, it is verified that the tracking error can converge to a certain range near zero in a finite time. Finally, the proposed control scheme is applied to ODIN AUV and compared with another two finite-time tracking controllers. Simulation results show the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper investigates the trajectory tracking control problem for an autonomous underwater vehicle (AUV) subject to the external disturbance, modelling uncertainty and an initial tracking error. A trajectory tracking control strategy is proposed based on a virtual closed-loop system. The proposed control architecture is based on a virtual closed-loop system used to generate the virtual reference trajectory that the AUV should follow instead of the original desired trajectory. At first, in the virtual closed-loop system, a fixed feedback controller is designed based on an approximation dynamic model of the AUV to relieve the chattering phenomenon of control input at the early stage. Then, a robust controller based on a generalised saturation function is combined with neural network estimation to allow the AUV to track the generated virtual reference trajectory. Finally, simulations with different trajectories are performed on an AUV to evaluate the performance of the new design. [ABSTRACT FROM AUTHOR]

This article presents a novel thruster fault diagnosis approach for an autonomous underwater vehicle. In the novel approach, a time-frequency entropy enhancement is used to extract feature, and then a boundary constraint–assisted relative gray relational grade is applied to identify thruster fault. The time-frequency entropy enhancement is developed from the smoothed pseudo Wigner–Ville distribution combined with Shannon entropy. First, the energy distributions of autonomous underwater vehicle dynamic signals are given in the time-frequency plane. And then the energy concentration in the energy distribution is enhanced based on a serial signal processing, including wavelet decomposition, modified Bayes' classification algorithm, and two dimensional convolution operation, successively. After that the Shannon entropy of the energy distribution is calculated. The boundary constraint–assisted relative gray relational grade comes from the gray relational analysis. A mapping function between the relative gray relational grade and the fault severity is established. And then the boundary constraints of relative gray relational grades at each standard fault level are determined. Moreover, the mapping function is modified based on the boundary constraints. Experiments are performed on an experimental prototype autonomous underwater vehicle in a pool. The experimental results demonstrate the effectiveness of the developed approaches in terms of improving the sensitivity of the fault feature to the fault severity, compared with the smoothed pseudo Wigner–Ville distribution combined with Shannon entropy, and increasing the identification accuracy, compared with the gray relational analysis. [ABSTRACT FROM AUTHOR]

Fault feature extraction is the basis of fault diagnosis. In the typical methods for AUV (autonomous underwater vehicle) thruster fault feature extraction, the local fault features of multiple input signals are extracted by using the modified Bayesian method, and the global fault feature is obtained by fusing these local fault features based on the evidence theory method. However, there are two problems based on the typical method. Firstly, there exists a large lag between fault detection time and occurrence time. Secondly, it is unable to extract effective global fault feature when the fault degree is low, that is, the global fault feature value is less than the interference. To solve these two problems, this paper develops the global fault feature based on two-stage fusion. Specifically in the first stage, multiple fault features based on thrust deviation are fused. In the second stage, local fault features are fused to obtain global fault features based on the cross-correlation method. The effectiveness of the proposed method is verified by AUV pool experiment. • Fault feature extraction method is proposed based on two-stage fusion from multi-source information. • The local fault features are extracted developed based on thrust deviation and multi-time window Fourier transform. • The third local fault feature is obtained based on cross-correlation. • Determine the fault occurrence time relatively accurately. • The ratio of fault features to interference features is high under weak fault conditions. [ABSTRACT FROM AUTHOR]

This paper investigates adaptive fault tolerant control and fault reconstruction problem for AUV subject to ocean current disturbance and modelling uncertainty. At first, a virtual closed-loop system based adaptive fault tolerant control method is developed. In this method, the constructed virtual closed-loop system is mainly used to deal with the influence of the initial tracking error in an ideal environment and avoid the serious chattering phenomenon in control output. Then with respect to fault reconstruction in the framework of fault tolerant control, an improved second-order sliding mode observer is constructed to estimate the thruster fault effect. The feedback of the developed observer consists of different functions of the estimation errors, including fractional function, signature function and integral function etc. Furthermore, the stability analysis is given based on Lyapunov theory. Finally, a series of simulations are performed on an over-actuated AUV for different desired trajectories and different types of thruster faults under the condition of the simulated ocean current environment. The comparative simulation results demonstrate the effectiveness and feasibility of the new design. [ABSTRACT FROM AUTHOR]

There exist some problems when the fractal feature method is applied to identify thruster faults for autonomous underwater vehicles (AUVs). Sometimes it could not identify the thruster fault, or the identification error is large, even the identification results are not consistent for the repeated experiments. The paper analyzes the reasons resulting in these above problems according to the experiments on AUV prototype with thruster faults. On the basis of these analyses, in order to overcome the above deficiency, an improved fractal feature integrated with wavelet decomposition identification method is proposed for AUV with thruster fault. Different from the fractal feature method where the signal extraction and fault identification are completed in the time domain, the paper makes use of the time-domain and frequent-domain information to identify thruster faults. In the paper, the thruster fault could be mapped multisource and described redundantly by the fault feature matrix constructed based on the time-domain and frequent-domain information. In the process of identification, different from the fractal feature method where the fault is identified based on fault identification model, the fault sample bank is built at first in the paper, and then pattern recognition is achieved by calculating the relative coefficients between the constructed fault feature matrix and the elements in the fault sample bank. Finally, the online pool experiments are performed on an AUV prototype, and the effectiveness of the proposed method is demonstrated in comparison with the fractal feature method. [ABSTRACT FROM AUTHOR]

A region tracking fault tolerant control approach based on backstepping technique is proposed for Autonomous Underwater Vehicles (AUV). The proposed approach aims at driving tracking error to reach into the desired region in presence of ocean current disturbance, modelling uncertainty, unknown thruster faults and thruster amplitude and rate saturation constraints. At first, a type of piecewise and differential Lyapunov function is constructed to achieve region tracking control in the frame of backstepping technique. Then, the paper analyses and acquires the bound structures of the lumped uncertainty (including ocean current disturbance and model uncertainty) and the variation of thruster distribution matrix caused by unknown thruster faults, respectively. An adaptive technique is used to estimate the unknown coefficients in the above bound structures. In addition, an adaptive adjustment scheme for the desired trajectory is developed to achieve region tracking control with thruster amplitude and rate saturation constraints. The stability of the closed-loop system is analysed based on Barbalat's lemma. Finally, simulations and pool-experiments are presented to illustrate the effectiveness of the proposed method. [ABSTRACT FROM PUBLISHER]

This article presents a novel thruster fault feature extraction method for autonomous underwater vehicle in time-varying ocean currents. The novel method is based on the combination of ensemble empirical mode decomposition and independent component analysis, which is a universal single-channel blind source separation method. When using ensemble empirical mode decomposition to decompose the autonomous underwater vehicle surge speed signal into a set of intrinsic mode functions, the original number of intrinsic mode functions is big, making the independent components that are separated and reconstructed from the intrinsic mode functions based on independent component analysis contain many false components. To reduce the original number of intrinsic mode functions, a method combining wavelet decomposition with empirical mode decomposition is proposed. When extracting fault feature value from independent components based on modified Bayes’ classification algorithm, the results show that the difference of fault feature value and noise feature value is small, and the ratio of fault feature value and noise feature value is small as well, which make it difficult to distinguish fault feature value from noise feature value. To increase the difference and the ratio, a wavelet detail component–assisted feature extraction method is proposed. The method combining wavelet decomposition with empirical mode decomposition and the wavelet detail component–assisted feature extraction method together constitute a novel thruster fault feature extraction approach which is suitable for autonomous underwater vehicle in time-varying ocean currents. The effectiveness of the proposed methods is verified by the pool experiments of the experimental prototype. [ABSTRACT FROM AUTHOR]

• A quaternion-based adaptive non-singular terminal sliding mode control scheme is proposed for underwater vehicles. • A quaternion representation is used to overcome the potential singularity of the transformation matrix. • Both sliding mode manifold and tracking error can converge to a small range near zero in finite time. This paper addresses trajectory tracking control problem for autonomous underwater vehicles (AUVs) using quaternion-based attitude representation. A quaternion based adaptive non-singular terminal sliding mode control scheme is proposed for an AUV subject to ocean current and modelling uncertainty. At first, kinematic equations of the translational and rotational motions are described by unit quaternions. And then, a class of modified non-singular terminal sliding mode surface is developed by smoothly switching into a non-singular sliding mode surface formed by sinusoidal functions when the tracking error reaches a small range of the origin. The finite-time convergences of both the position tracking error and quaternion error are proved based on Lyapunov theory. Finally, the proposed control scheme is applied on a typical fully-actuated AUV to perform simulations. And the simulation results illustrate the effectiveness of the proposed control scheme in comparison with another terminal sliding mode control algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a novel tube model predictive control approach for dynamic positioning of autonomous underwater vehicles with state and input constraints. The cost function is selected as a mixture of weighted 1-norms and ∞-norms of states and inputs, which can be reconstructed into linear summation form. Specially, a method is presented to calculate the weighted matrix of the terminal cost so that closed-loop stability is achieved. The proposed control problem can be solved by linear programming, which provides high computational efficiency. Experimental results on “UVIC-I” AUV are conducted to validate the viability and validity of the presented method. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of region tracking control with prescribed transient performance for autonomous underwater vehicles (AUVs) in presence of ocean current and thruster fault. An adaptive region tracking control scheme with prescribed transient performance is proposed. On the basis of prescribed performance control concept, the proposed control scheme transforms the tracking error into a virtual error by combining with a prescribed performance function. And then the control law is derived by constructing a type piecewise and smooth Lyapunov function in the framework of backstepping technique. Moreover, the compensation of ocean current, modelling uncertainty and thruster fault is achieved by estimating the bound of the general uncertainty term and the variation of the thruster distribution matrixes. Finally, the proposed region tracking control scheme is applied on ODIN AUV, and the simulation results verify the effectiveness of the proposed method. • A region tracking FTC scheme with prescribed transient performance is proposed. • Ocean current and thruster fault is compensated by on-line adaptive technique. • The proposed region tracking control scheme is verified by simulation results. [ABSTRACT FROM AUTHOR]