  Estimating and detecting faults is crucial in ensuring safe and efficient
automated systems. In the presence of disturbances, noise or varying system
dynamics, such estimation is even more challenging. To address this challenge,
this article proposes a novel filter to estimate multiple fault signals for a
class of discrete-time linear parameter-varying (LPV) systems. The design of
such a filter is formulated as an optimization problem and is solved
recursively, while the system dynamics may vary over time. Conditions for
existence and detectability of the fault are introduced and the problem is
formulated and solved using the quadratic programming framework. We further
propose an approximate scheme that can be arbitrarily precise while it enjoys
an analytical solution, which supports real-time implementation. The method is
illustrated and validated on an automated vehicle's lateral dynamics, which is
a practically relevant example for LPV systems. The results show that the
estimation filter can decouple unknown disturbances and known or measurable
parameter variations in the dynamics while estimating the unknown fault.
