Fault Diagnosis And Fault-tolerant Control Of Autonomous Underwater Vehicle

The working condition of autonomous underwater vehicle (AUV) is uncertain andcomplex and the manual intervention to the vehicle is limited, then it’s unavoidable thatthere are all kinds of faults appearing in this system. What’s more, some serious faultsmay lead a lost of the vehicle. So it’s necessary to research fault detecting and diagnosis(FDD) and fault-tolerant control (FTC) system for autonomous underwater vehicle. Inthis paper, we have proposed and verified some methods to diagnosis actuator faults andsensor faults, and have research FTC measures overcome actuator faults. These methodsand measures are useful in engineering application and theoretical significant.Firstly, through dynamics and kinematics analysis of autonomous underwater vehicle,we make the hydro-dynamic model of this platform and found the kinematics equation inthe geodetic coordinate system. Then, get the state equation of motion for AUV. Basingon these works, we can build actuator fault models and sensor fault models.In the actuator fault models, the control inputs make up of two parts: one is theexpected control forces or moments and the other one is the control force or momentlosses, the results of actuator fault. We look on theses losses as extended states of theplatform and estimate them together with the velocity and position states by nonlinearsystem state estimation method based on the Gaussian particle filter. The sequences ofloss estimations are detected by Modified Bays method to find the fail and SlidingWindow method is used to estimate the amplitude of control force or moment losses.According to relationship between control forces or moments and actuators, Faults arepositioned and separated combining with actuators used by system at present moment. Asimulation of actuator FDD on the horizontal motion surface is conducted and the resultsshow that the method could realize the actuator fault diagnosis. Another trial proves thatthe method is feasible and effective.We can get the prior estimation of motion states through the nonlinear system stateestimation based on the Gaussian particle filter, and then the prior estimations of outputsis calculated. We compare the estimation of outputs with measured value to get theresidual sequence which will be detected by Threshold value analysis to diagnose thesensor faults. The effectiveness is verified by means of simulation, and the simulation results also show that the method is robust to actuator faults and ambient noise.We study the thrust distribution method of ZS, an autonomous underwater vehicle inmy Lab., then redistribute the control on the basis of generalized inverse thrustdistribution. The main strategies to redistribution are changing the bound value ofthrusters, the thruster weights and the matrix of thruster configuration. A simulationsystem of FDD and FTC to actuator faults is founded for ZS in MATLAB/Simulinkenvironment. The simulation results show that FDD method could diagnose the actuatorfaults added before and FTC measures could effectively reduce impact of actuator faultson performance of motion control.