Enhanced model-based experiment design techniques for parameter identification in complex dynamic systems under uncertainty
Seminar Room 1, Newton Institute
AbstractA wide class of physical systems can be described by dynamic deterministic models expressed in the form of systems of differential and algebraic equations. Once a dynamic model structure is found adequate to represent a physical system, a set of identification experiments needs to be carried out to estimate the set of parameters of the model in the most precise and accurate way. Model-based design of experiments (MBDoE) techniques represent a valuable tool for the rapid assessment and development of dynamic deterministic models, allowing for the maximisation of the information content of the experiments in order to support and improve the parameter identification task. However, uncertainty in the model parameters or in the model structure itself or in the representation of the experimental facility may lead to design procedures that turn out to be scarcely informative. Additionally, constraints may occur to be violated, thus making the experiment unfeasible or even unsafe. Handling uncertainty is a complex and still open problem, although over the last years significant research effort has been devoted to tackle some issues in this area. Here, some approaches developed at CAPE-Lab at University of Padova will be critically discussed. First Online Model-Based Redesign of Experiment (OMBRE) strategies will be taken into account. In OMBRE the objective is to exploit the information as soon as soon as it is generated by the running experiment. The manipulated input profiles of the running experiment are updated by performing one or more intermediate experiment designs (i.e., redesigns), and each redesign is performed adopting the current value of the parameter set. In addition, a model updating policy including disturbance estimation embedded within an OMBRE strategy (DE-OMBRE) can be considered. In the DE-OMBRE approach, an augmented model lumping the effect of systematic errors is considered to estimate both the states and the system outputs in a given time frame, updating the constraint conditions in a consistent way as soon as the effect of unknown disturbances propagates in the system. Backoff-based MBDoE, where uncertainty is explicitly accounted for so as to plan a test that is both optimally informative and safe by design, is eventually discussed.
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