A stochastic embedding approach for quantifying uncertainty in the estimation of restricted complexity models

This paper presents a new approach to the understanding and treatment of the effects of undermodelling in the estimation of models for dynamical systems. There already exists a substantial body of knowledge dealing with the implications of undermodelling on robust control system design. However, the corresponding problem for model estimation has received only scant attention. This paper presents a general conceptual and philosophical framework which adds the important dimension of undermodelling to the estimation problem. The philosophy of the approach is to embed the nominal model structure into a larger class of models which includes a generic characterization of undermodelling. This broader perspective allows the effects of limited model complexity on estimation to be quantified. The implications of these results are believed to be wide‐ranging and include new insights into the role of user choices and experimental conditions in estimation, integration of robust and adaptive control, new strategies for fault detection and diagnosis, and so on.