  In this article, we propose a diagnosis filter to estimate multiple fault
signals for a class of discrete-time linear parameter-varying (LPV) systems.
The design of such a filter can be formulated as an optimization problem to be
solved recursively, while the system dynamics vary over time. The feasibility
of such a problem is governed by given conditions on the detectability of the
fault of interest. By formulating this convex problem in the quadratic
programming framework, an analytical solution can be derived to approximate an
optimal diagnosis filter at each time instance, which supports real-time
implementation. The proposed methodology is illustrated and validated through
an application in the lateral dynamics of an automated vehicle which is an
often used example for this particular type of model class. The numerical
results show that the diagnosis filter can decouple unknown disturbances and
known or measurable parameter variations in the dynamics while detecting the
unknown fault of interest.
