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We propose a systematic method to directly identify a sensor fault estimationfilter from plant input/output data collected under fault-free condition. Thisproblem is challenging, especially when omitting the step of building anexplicit state-space plant model in data-driven design, because the inverse ofthe underlying plant dynamics is required and needs to be stable. We show thatit is possible to address this problem by relying on a system-inversion-basedfault estimation filter that is parameterized using identified Markovparameters. Our novel data-driven approach improves estimation performance byavoiding the propagation of model reduction errors originating fromidentification of the state-space plant model into the designed filter.Furthermore, it allows additional design freedom to stabilize the obtainedfilter under the same stabilizability condition as the existing model-basedsystem inversion. This crucial property enables its application to sensorfaults in unstable plants, where existing data-driven filter designs could notbe applied so far due to the lack of such stability guarantees (even afterstabilizing the closed-loop system). A numerical simulation example of sensorfaults in an unstable aircraft system illustrates the effectiveness of theproposed new method.

Direct identification of fault estimation filter for sensor faults