Abstract
This paper introduces a novel thruster fault diagnosis and accommodation system for open-frame underwater vehicles with abrupt faults. The proposed system consists of two subsystems: a fault diagnosis subsystem and a fault accommodation sub-system. In the fault diagnosis subsystem a ICMAC(Improved Credit Assignment Cerebellar Model Articulation Controllers) neural network is used to realize the on-line fault identification and the weighting matrix computation. The fault accommodation subsystem uses a control algorithm based on weighted pseudo-inverse to find the solution of the control allocation problem. To illustrate the proposed method effective, simulation example, under multi-uncertain abrupt faults, is given in the paper.
Highlights
Thrusters of UV(underwater vehicles) are one of the most common and most important sources of faults, and its faults always make direct effect in the control performance, FTC(fault-tolerant control)for thruster of UV is a research hot topic nowadays (Gianluca,A.,2006)
The system was subdivided into individual fault tolerant subsystems for dealing with thruster and sensor failures separately
Only a whole(total) fault of the thruster was considered in these researches, and scope of actual applications and control effects of FTC are poor
Summary
Normal state and three different fault cases were considered Control signals given by Fault Accommodation Unit, can be transferred to normal thruster model and actual faults thruster respectively. Once an abnormal system behavior is detected by the fault detection subsystem, an online estimator initially starts to learn the failure by using the difference between the desired output and actual output as the desired target of the ICMAC network, and after some steps of training the network will output the forecast value of fault, no matter what kind of fault ( i.e. some state range between jammed and heavy jammed), and the. The control law is reconfigured by the fault accommodation unit based on the current knowledge of the failure dynamics provided by the online estimator ICMAC.
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