Quality Assessment of Models for Iterative/Adaptive Control

This paper deals with the problem of validating a model within the framework of iterative identification and control design. Regarding model validation, iterative control arises the following requirements: i) Is it possible to improve an existing model? Are the data informative enough to attempt a new identification? ii) How can the model be improved? Is the model order/structure rich enough to capture the interesting features of the plant? iii) How authoritative can be the controller designed? However classical validation methods "accept" or "reject" a model as a valid representation of a plant and can not answer the required questions when applied on iterative identification and control schemes (e.g. in iterative identification and control approaches a low order model is fitted to capture the frequency range of interest for control. Hence undermodelling is always present. This fact makes it difficult to apply traditional model validation schemes.). We introduce a new validation procedure to iterative control which gives answer to the above questions. The validation procedure is more informative due to its frequency information content. Moreover the validation procedure is no longer a static test but a dynamic one which gives guidelines during the iterative identification and control procedure