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This paper investigates the filter design problem for automotive controlled suspensions when no mathematical model of the system is available, but a set of initial experiments can be performed, where also the variable to be estimated is measured. The problem of designing suitable linear time-invariant filters is here investigated, focusing the attention on the estimation of the relative vertical speed...
A relevant issue in filter design is that, in most practical situations, the system whose variables have to be estimated is not known, and a two-step procedure is adopted, based on model identification from data and filter design from the identified model. However, only approximate models can be identified from real data, and this approximation may lead to large estimation errors. In this paper, a...
Optimal filters for nonlinear systems are in general difficult to derive/implement. The common approach is to obtain approximate solutions, e.g. based on linearizations along the performed path, as done in extended Kalman filters. However, no optimality properties can be guaranteed using these approximations, not even the stability of the estimation error is ensured. In this paper, a new method is...
Direct identification of filters for linear parameter varying (LPV) systems is considered. In the literature on filter design, the system whose state has to be estimated is usually assumed known. However, in most applications, this assumption does not hold, and a two-step procedure is adopted: 1) an LPV model is identified from a set of noise-corrupted data; 2) on the basis of the identified model,...
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