In this paper we propose a Fault Detection and Isolation (FDI) filter design method for Spark Injection Engines. Starting from a detailed nonlinear Mean-value engine mathematical representation, a LPV approximation based on a judicious convex interpolation of a family of linearized models is obtained. An LPV-FDI filter based on the Luenberger observer theory is synthesized by ensuring guaranteed levels of disturbance rejection and fault detection and isolation. The resulting diagnostic filter is parameter-dependent and uses a set of scheduling engine parameters, assumed measurable on-line. The effectiveness of the LPV-FDI framework is illustrated by numerical examples. The obtained LPV approximation is here validated and the diagnostic capabilities of the proposed FDI architecture proved.
A LPV Fault Detection and Isolation method for a Spark Injection Engine
Gagliardi G;CASAVOLA, Alessandro;FAMULARO, Domenico;Franzé, G.
2010-01-01
Abstract
In this paper we propose a Fault Detection and Isolation (FDI) filter design method for Spark Injection Engines. Starting from a detailed nonlinear Mean-value engine mathematical representation, a LPV approximation based on a judicious convex interpolation of a family of linearized models is obtained. An LPV-FDI filter based on the Luenberger observer theory is synthesized by ensuring guaranteed levels of disturbance rejection and fault detection and isolation. The resulting diagnostic filter is parameter-dependent and uses a set of scheduling engine parameters, assumed measurable on-line. The effectiveness of the LPV-FDI framework is illustrated by numerical examples. The obtained LPV approximation is here validated and the diagnostic capabilities of the proposed FDI architecture proved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.