Abstract
In real-world applications, the traditional approach to filter design is based on a two-step procedure: (1) model identification from data; (2) filter design from the identified model. However, the two-step procedure is in general not optimal and evaluating the effects of the modeling error (occurring in the identification step) on the estimation error of the designed filter is a largely open problem. In this paper, a new approach to filter design for LPV systems is proposed, based on the direct design of the filter from data, avoiding the intermediate step of model identification. This approach, developed within a Set Membership framework, allows the design of optimal filters and the evaluation of non-conservative bounds on the estimation error. An applicative example is presented, related to the estimation of vehicle yaw rate, a variable used by safety control systems to improve the vehicle stability.
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