Diagnosis of actuator faults using LPV-gain scheduling techniques

Abstract

We present a new approach for the synthesis of robust fault detection filters for the model based diagnosis of actuator faults. The underlying synthesis model is a linear parameter varying (LPV) description obtained using a combination of analytic and numerical parameter fitting techniques. The actuator LPV model contains uncertain parameters which are partly measurable, thus can be used for gain scheduling, and partly not measurable, for which robustness must be ensured. The detector synthesis approach is basically a parametric nullspace method combined with min-max parameter fitting. The synthesis technique is applied to the diagnosis of two classes of actuator faults for a civil aircraft: elevator runaway and elevator jamming at null position.