Two recursive algorithms are presented for estimating state variables of observable linear time-invariant continuous-time dynamical systems from the input-output information using two classes of OFs, namely BPFs and SLPs. The principle of the Luenberger observer is utilized for estimating the state variables. The followed approach has the distinct advantage that the smoothing effect of integration reduces the influence of zero-mean observation noise on estimation. Results of a simulation study on two examples indicate that the recursive algorithms work well.
State estimation plays an important role in the context of state feedback control as it requires complete and accurate information of all state ...
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