Baoji Yin

Adaptive terminal sliding mode based thruster fault tolerant control for underwater vehicle in time-varying ocean currents

Abstract The paper focuses on fault tolerant control for underwater vehicles in time-varying ocean environment subject to external disturbance, modeling uncertainty, and unknown thruster fault. A fault tolerant control method for underwater vehicle with thruster fault is proposed based on adaptive terminal sliding mode. Adaptive strategy is incorporated into terminal sliding mode to estimate on-line the upper bounds of the lumped uncertainties, including ocean current disturbance and modeling uncertainty, and the change of thruster distribution gain caused by thruster fault, respectively. The great advantages of the proposed method are that the prior knowledge of the lumped uncertainty is not required and it is independent of fault detection and diagnosis (FDD) module. Based on Lyapunov theory and Barbalat׳s lemma, the proposed method can accommodate thruster fault, and ensure the finite-time stability of the tracking error. Furthermore, with respect to the chattering phenomenon, a continuous switching term based on fractional power is developed in place of the discontinuous switching term. In the proposed chattering-reduction method, the continuity of switching term is achieved based on fractional power, and the gain of the proposed switching term is updated based on the exponential form of Euclidean-norm of sliding mode function. Finally, simulations and pool-experiments of underwater vehicles are carried out to demonstrate the effectiveness and feasibility of the proposed method.

Thruster fault feature extraction for autonomous underwater vehicle in time-varying ocean currents based on single-channel blind source separation

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.

Thruster fault identification method for autonomous underwater vehicle using peak region energy and least square grey relational grade

A novel thruster fault identification method for autonomous underwater vehicle is presented in this article. It uses the proposed peak region energy method to extract fault feature and uses the proposed least square grey relational grade method to estimate fault degree. The peak region energy method is developed from fusion feature modulus maximum method. It applies the fusion feature modulus maximum method to get fusion feature and then regards the maximum of peak region energy in the convolution operation results of fusion feature as fault feature. The least square grey relational grade method is developed from grey relational analysis algorithm. It determines the fault degree interval by the grey relational analysis algorithm and then estimates fault degree in the interval by least square algorithm. Pool experiments of the experimental prototype are conducted to verify the effectiveness of the proposed methods. The experimental results show that the fault feature extracted by the peak region energy method is monotonic to fault degree while the one extracted by the fusion feature modulus maximum method is not. The least square grey relational grade method can further get an estimation result between adjacent standard fault degrees while the estimation result of the grey relational analysis algorithm is just one of the standard fault degrees.

Fault degree identification method for thruster of autonomous underwater vehicle using homomorphic membership function and low frequency trend prediction

This article presents a novel thruster fault degree identification method for autonomous underwater vehicle. The novel method is developed from the fuzzy support vector domain description method, which establishes a fault identification model first, and then estimates fault degree according to the model. When establishing fault identification model for thruster based on fuzzy support vector domain description method, it is found that the relative fitting error of the model to the actual fault degree is large, making the model accuracy poor. To reduce the relative fitting error, a homomorphic membership function method is proposed. Different from fuzzy support vector domain description method, which calculates the fuzzy membership degree of fault sample in time domain, the proposed method calculates the fuzzy membership degree in log domain. On estimating thruster fault degree by fuzzy support vector domain description method, it is obtained that the estimated fault degree lags behind the actual fault degree. To shorten the lag time, a low frequency trend prediction method is proposed. Different from fuzzy support vector domain description method, which brings the fault feature extracted from the current surge speed and control voltage into the fault identification model to calculate fault degree, the proposed method firstly forward predicts surge speed and control voltage, and then takes the fault feature extracted from the predicted surge speed and control voltage into the model to acquire fault degree. The effectiveness of the proposed methods is verified by pool experiments of the experimental prototype.

Thruster fault identification based on fractal feature and multiresolution wavelet decomposition for autonomous underwater vehicle

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.