Finite-horizon input selection for system identification

The accuracy of an identified model depends on the choice of input signal. Persistency of excitation is a necessary criterion for such signals. In this paper we develop additional criteria for input signal selection, in particular, the input at each time step is chosen to minimize the predicted variance of the system estimate at the next time step. We extend the method to the finite-horizon input selection problem and demonstrate the method in simulation.

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