Choosing Between Open- and Closed-Loop Experiments in Linear System Identification

This correspondence shows that open-loop experiments are optimal for a broad class of systems when the system input is constrained. In addition, we show that, for a general class of systems, when the output power is constrained, closed-loop experiments are optimal. Both results use a strong notion of optimality and use expressions for estimation accuracy which are nonasymptotic in model order but asymptotic in data length.