X. Zha and F. Crusca (Australia)
Robust control; Fault isolation and estimation; Linear matrix inequality (LMI); Norm-bounded uncertainties; Robust quadratic performance; Convex optimization
This paper investigates the fault isolation and estimation problem for linear time-invariant systems which subjected to a class of norm-bounded model uncertainties. Rather than using a reference model and transfer the fault detection problem into a model-watching formulation, we provide a direct fault reconstruction approach to estimate the incoming fault signal. Unknown input, actuator faults, sensor faults and disturbances are considered in our design. A sufficient affine matrix inequality condition is developed which guarantees that the filter estimation error is kept below a specified level of performance. All specified model uncertainties includes unstructured, structured, time-invariant, time-varying and nonlinear types has been tested in our paper. In order to reduce our nonlinear matrix inequality into an equivalent AMI, we define certain congruence transformation parameters and provide an interior-point convex optimization method to obtain the convex solution. A helicopter model is applied in the paper to confirm the effectiveness of our approach.
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