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Least costly identification experiment for control

Project members:  X.J.A. Bombois, M. Gevers (Université Catholique de Louvain, Belgium), G. Scorletti (Université de Caen, France), prof. P.M.J. Van den Hof, H. Hjalmarsson (KTH, Sweden), M. Barenthin (KTH, Sweden), M. Gilson (CRAN, France)
Keywords:  Identification and estimation, Data driven and fault tolerant control, Process technology, Old - project will be removed
Model-based control has nowadays reached many industrial sectors (chemical industry, high-tech manufacturing industry, ...) as a crucial technology in realizing optimal process operation. However, the development of model-based control is still restrained by various open issues in control and system theory. In most of these issues, the harmonious interaction between system identification and robust control design is very important. Since the beginning of the nineties, important steps have been taken towards this harmonious interaction. Indeed, it is now possible, given a model and its related uncertainty as delivered by system identification, to check whether a controller meets the robust performance requirements. However, until very recently, there was still a complete lack of results allowing the design of robust controllers based on cheap and plant-friendly identification. Indeed, until then, very few attention had been devoted on the choice of the data used to identify the model and its uncertainty region and the lack of clear guidelines for the choice of these data generally lead to identification experiments that were more expensive than actually necessary, i.e. the identified uncertainty region was either too large (and thus unusable) or smaller than strictly necessary for the required level of performance. Knowing that the generation of informative data for an identification experiment is the most expensive step of the whole robust control design procedure, the related economic loss could be very important.

Based on these considerations, a brandnew line of research has recently been defined. This research aims at developing tractable techniques for an optimal design of the identification experiment. In particular, the objective is to determine the least costly identification experiment that delivers sufficient information on the true system dynamics (i.e. that delivers a model with a sufficiently small uncertainty region) for the design of a robust controller with a satisfactory performance.

In particular, the influence of short data sets and the extension of the concepts towards performance monitoring and robust filtering are currently investigated.

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