On The Key Techniques For Fault Tolerance In AUV Integrated Navigation System

As important instruments for exploring and exploiting the oceans,autonomous underwater vehicles(AUVs)are of great significance in our country's ocean strategy.The navigation system of an AUV provides position,velocity and attitude information.Accurate and reliable navigation information of the AUV is prerequisite to the successful execution of the tasks.This dissertation focuses on the fault-tolerant technology for AUV integrated navigation systems.System design,information fusion of asynchronous multi-sensors,fault detection,the approach to deal with short-time sensor malfunctions,fault-tolerant filtering for AUV integrated navigation systems are studied.The main research work and results are as follow:1.In order to meet the sound reliabiltiy of the AUV navigation system,the structure of the fault-tolerant system is designed by using no-feedback federated filter.Fault detetion module and fault isolation decision module which are used to avoid the cross contamination of faults are set up for each local filter.The working principles of the common navigation sensors or systems for AUVs are analyzed.With the strapdown inertial navigation system(SINS)as the reference system,the error model of the AUV navigation system is build.2.An information fusion algorithm based on multi-scale estimation theory is proposed to deal with the situation that multi-sensors measure with different rates.In the case of without the measurement delay,the multi-scale system model is recursively got by mathematical induction.By judging whether the measurement information exists,respective fusion algorithms are derived.In the case of with the measurement delay,the multi-scale system model is obtained by partitioning and extending the state and measurement information.The target state parameters are estimated by employing the measurements on different scales.The fusion algorithm is deduced in units of data block.The proposed algorithms are used in an AUV integrated navigation system composed of asynchronous SINS,DVL and TAN.The simulation results show that the multi-scale information fusion algorithms have the higher degree of accuracy compared with the single-scale information fusion algorithms.Then the proposed algorithms can improve the positioning accuracy of AUV navigation systems with asynchronous multi-sensors.3.Fault detection methods are studied as many uncertainties in AUV workplaces may affect the stability and accuracy of the sensors.A novel fault detection method using Gaussian process regression(GPR)is proposed to solve the problem that the gradual fault is difficult to detect timely.To avoid the local optimization,particle swarm optimization is introduced to find the optimal hyper-parameters of GPR model.The GPR model is used to predict the innovation of Kalman filter.Then a novel fault detection function(FDF)is build by employing the predicted innovations.The structure of the FDF help enlarges the difference between the FDF value of fault-free system and that of failing system.The semi-physical simulation shows that the proposed method can detect the gradual fault more quickly compared with the residual chi-squard test.Thus the navigation systems with the proposed method can quickly make a judgment and then handle the failures,which enhance the correctness of the navigation results.4.As a common device for AUV navigation systems,the DVL has the high risk of short-time malfunctions.To solve the problem,a novel hybrid approach is presented.The approach employs partial least squares regression(PLSR)coupled with support vector regression(SVR)to build a hybrid predictor.During the DVL malfunctions,the hybrid predictor offers the estimation of the DVL measurements for information fusion.As the current and past calculating velocities of SINS are taken as the predictor's inputs,PLSR is applied to cope with the situation where there exists intense relativity among independent variables.Since PLSR is a linear regression,SVR is used to predict the residual components of the PLSR prediction to improve the accuracy.The mathematical simulation and the semi-physical simulation show that the PLSR-SVR hybrid predictor can correctly provide the estimated DVL measurements and effectively extend the tolerance time on DVL malfunctions,thereby improving the accuracy and reliability of the navigation results.5.To ensure the security and reliability of AUVs,an intelligent fault-tolerant filtering algorithm based on fuzzy logic is proposed.As insufficiently known priori statistics will reduce the precision of the state estimates,fuzzy logic is employed to adaptively adjust the measurement covariance matrixes of local filters online.The confidence of measurement is introduced as the regulatory factor to realize the fault isolation.The algorithm flow of the fault-tolerant filtering for AUV integrated navigation systems is designed.The simulation based on SINS/MCP/DVL/TAN integrated navigation system shows that,the proposed filtering algorithm detects and insulates both the abrupt fault and the gradual fault effectively,which enhances the fault-tolerance of the system.Based on the above research and innovative design,the accuracy and fault-tolerance of AUV integrated navigation systems have been greatly improved which helps to enhance the reliability and security of AUVs.