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Many systems can be modelled as networks of interconnected continuous-time transfer functions. The coefficients of the transfer functions in the network are often directly related to physical properties of the system. Thus, the question we consider is: under what conditions is it possible to obtain a consistent estimate of a continuous-time transfer function embedded in a dynamic network? We consider both direct and indirect continuous-time approaches. We show that a discrete-time model of a continuous-time data generating system may have a different interconnection structure (due to aliasing) and may have algebraic loops (due to the intersample behaviour). Subsequently we present an instrumental variable based method to directly identify a continuous-time transfer function embedded in a network.

An Experiment Design framework for dynamical systems which execute multiple batches is presented in this paper. After each batch, a model of the system dynamics is refined using the measured data. This model is used to synthesize the controller that will be applied in the next batch. Excitation signals may be injected into the system during each batch. From one hand, perturbing the system worsens the control performance during the current batch. On the other hand, the more informative data set will lead to a better identified model for the following batches. The role of Experiment Design is to choose the proper excitation signals in order to optimize a certain performance criterion defined on the set of batches that is scheduled. A total cost is defined in terms of the excitation and the application cost altogether. The excitation signals are designed by minimizing the total cost in a worst case sense. The Experiment Design is formulated as a Convex Optimization problem which can be solved efficiently using standard algorithms. The applicability of the method is demonstrated in a simulation study.

The problem of identifying dynamical models on the basis of measurement data is usually considered in a classical open-loop or closed-loop setting. In this paper this problem is generalized to linear dynamical systems that operate in a complex interconnection structure and the dynamical relationships between the measured variables signals need to be identified. It is shown that the classical Direct Method of closed-loop identification in the prediction-error context can be generalized to provide consistent model estimates, under specified experimental circumstances.

Two batch-to-batch (B2B) algorithms for supersaturation control in cooling crystallization are presented in this paper. In Iterative Learning Control (ILC) a nominal process model is adjusted with an additive correction term which depends on the error in the last batch. In Iterative Identification Control (IIC) the physical parameters of the process model are recursively estimated by adopting a Bayesian identification framework. Both B2B algorithms compute an optimized input for the next batch that is fed to a lower level PI feedback controller in order to reject the process disturbances. The tracking performance of these B2B+PI control schemes is investigated in a simulation study.