This paper studies the problem of continuous
gain-scheduled robust fault detection (RFD) on a class of timedelay
stochastic nonlinear systems with partially known jump
rates. By means of gradient linearization procedure, stochastic
linear models and filter-based residual signal generators are constructed
in the vicinity of selected operating states. Furthermore,
in order to guarantee the sensitivity to faults and robustness
against unknown inputs, an RFD filter (RFDF) is designed for
such linear models by first designing H∞ filters that minimize the
influences of the disturbances and modeling uncertainties and then
a new performance index that increases the sensitivity to faults.
Subsequently, a sufficient condition on the existence of RFDF
is established in terms of linear matrix inequality techniques.
Finally, a continuous gain-scheduled approach is employed to
design continuous RFDFs on the entire nonlinear jump system. A
simulation example is given to illustrate that the proposed RFDF
can detect the faults correctly and shortly after the occurrences.