Andrew Hsu (Other), Pradeep Misra (Committee Member), Kuldip Rattan (Committee Member), Xiaodong Zhang (Advisor)
Master of Science in Engineering (MSEgr)
With the increase in the complexity of control systems design and the demand for more productivity, the possibility of the occurrence of faults in control systems has also significantly increased. In this thesis, a unified method for the fault diagnosis of sensor faults and process faults is developed for a class of Lipschitz nonlinear uncertain systems. The fault detection and isolation (FDI) architecture is comprised of a fault detection estimator and a bank of fault isolation estimators (FIEs), where each FIE is designed, based on the functional structure of a particular fault, in the fault class under consideration. The output residuals are generated, and adaptive thresholds are designed for the detection and isolation of the faults. The effectiveness of the fault detection and isolation algorithm is illustrated by a simulation example of single-link robotic arm. Extensive simulation studies have been conducted using Matlab/Simulink. Based on the nature of the residuals and their corresponding adaptive thresholds, the faults under consideration are successfully detected and isolated.
Department or Program
Department of Electrical Engineering
Year Degree Awarded
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