  This paper addresses the problem of robust process and sensor fault
reconstruction for nonlinear systems. The proposed method augments the system
dynamics with an approximated internal linear model of the combined
contribution of known nonlinearities and unknown faults -- leading to an
approximated linear model in the augmented state. We exploit the broad modeling
power of ultra-local models to characterize this internal dynamics. We use a
linear filter to reconstruct the augmented state (simultaneously estimating the
state of the original system and the sum of nonlinearities and faults). Having
this combined estimate, we can simply subtract the analytic expression of
nonlinearities from that of the corresponding estimate to reconstruct the fault
vector. Because the nonlinearity does not play a role in the filter dynamics
(it is only used as a static nonlinear output to estimate the fault), we can
avoid standard restrictive assumptions like globally (one-sided) Lipschitz
nonlinearities and/or the need for Lipschitz constants to carry out the filter
design. The filter synthesis is posed as a mixed H2/Hinf optimization problem
where the effect of disturbances and model mismatches is minimized in the Hinf
sense, for an acceptable H2 performance with respect to measurement noise.
