The application of advanced control methods to large-scale systems in variable industrial environment requires modeling and identification platform capable of keeping global model with description of its uncertainties, building global model from sub-systems, retrieval of sub-models with mutual consistency, model actualization from new sub-models or new data, etc. This article treats the problem of assembling global model for large-scale system from interconnected and possibly overlapping sub-models, i.e. there can be duplicity in the models. The quality of sub-models can also be different and is taken into account. The article presents two new results: merging of multiple models for the same system by using equivalent data and consistent combination of arbitrary connected models with parametric uncertainty into single model by using statistics of random vectors convolution.
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