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Fault tolerant control of rotor/magnetic bearing systems
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Dissertation
Fault tolerant control of rotor/magnetic bearing systems
1999
Overview
Magnetic bearings now exist in a variety of industrial applications. However, there still exist concerns over the fault tolerance and control integrity of rotor/magnetic bearing systems in general. Unless control systems can be developed that have the ability to maintain safe operation when the system is in an abnormal, degraded or faulty state then many, otherwise viable, magnetic bearing applications will remain unfulfilled.In this thesis, a variety of potential fault conditions are considered for flexible rotor systems. These conditions are classified into two main groups. The first comprises those that are external to the control system, for which tolerance can be achieved through appropriate controller design. It is shown that these faults can be modelled as system disturbances in the framework of robust controller design, and that optimal (H and H2) controllers can be designed to cope with such faults. The performance of such controllers under the designed fault conditions is assessed and compared with more standard control implementations.The second group of faults is considered internal to the control system, for which tolerance cannot be achieved through standard control design methods. A method is proposed for the detection and identification of such fault conditions using a single layer feed-forward neural network, running in real time. As a example it is demonstrated that the neural network can be trained to identify faults affecting the system position transducer measurements, and that the output from the neural network can be used as a decision tool for reconfiguring control. In this way satisfactory control of the system can be maintained during failure of a controller input. The method requires no knowledge of the system dynamics or system disturbances, and the network can be trained on-line.
Publisher
ProQuest Dissertations & Theses
Subject
ISBN
9781073238903, 1073238903
