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On the optimality of open and closed loop experiments in system identification

Abstract:

In this paper we analyse the strong optimality of open and closed loop experiments. In particular, we establish that when there is a constraint on the system input, then open loop experiments are optimal for a wide class of design criteria. Conversely, we show that when there is a constraint on the output power, then closed loop experiments are optimal for a class of systems and for a wide class of design criteria. Our analysis uses the non-asymptotic (in model order) variance expressions for dynamic systems. © 2006 IEEE.