Robust Identification and Fault Detection using Zonotope-based Direct and Inverse Approaches
نویسنده
چکیده
Abstract: This paper addresses the problem of robust identification/fault detection when a model with parametric modelling is considered. Two identification methods are introduced following, respectively, the worst-case and set-membership approaches. In particular those algorithms will use zonotopes to bound the parametric uncertainty. These two identification approaches lead to two robust fault detection tests: namely, the direct and inverse tests. Then, the underlying hypothesis of both approaches are also discussed and performance is compared using a model of the rain-gauges of the Barcelona sewer network.
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