This paper addresses the problem of obtaining an estimate of a particular module of interest that is embedded in a dynamic network with known interconnection structure. In this paper it is shown that there is considerable freedom as to which variables can be included as inputs to the predictor, while still obtaining consistent estimates of the particular module of interest. This freedom is encoded into sufficient conditions on the set of predictor inputs that allow for consistent identification of the module. The conditions can be used to design a sensor placement scheme, or to determine whether it is possible to obtain consistent estimates while refraining from measuring particular variables in the network. As identification methods the Direct and Two Stage Prediction-Error methods are considered. Algorithms are presented for checking the conditions using tools from graph theory.

We consider the input design problem of finding the minimal required experiment time such that accuracy constraints on the parameter estimate of an identification experiment are satisfied, while also respecting signal amplitude bounds. The input signal is parameterized as a multi-sine. We first show how multiple linear matrix inequalities from the least-costly and applications-oriented experiment design frameworks can be transformed into a generalised E-optimality constraint. Then, the solution to our problem is found by: (i) designing a multi-sine of one period with the Guillaume-Manchester algorithm [12], [10] that minimises the generalised E-optimality criterion under signal amplitude bounds, and (ii) utilising periodicity and an optimality condition to scale the experiment time such that the imposed accuracy constraints are also respected. An example shows an experiment time reduction of 50% compared with a traditional least-costly experiment design approach.