IDENTIFICATION OF FULL PROFILE DISTURBANCE MODELS FOR SHEET FORMING PROCESSES

In this article we present a method for the on-line identification and modeling of full profile disturbance models for sheet forming process es. A particular principal components analysis technique called the Ka rhunen-Loeve expansion is used to adaptively identify the significant features of the profile. In addition, we show how the temporal modes o f the reconstructed profile can be modeled using low-order linear auto regressive (AR) processes. By simulation examples, the effect of the o rder of the AR model is studied, as well as the window size of the dat a used in the on-line application of the KL expansion, the effect of d ata weighting, the importance of the correct selection of the number o f modes, and the frequency of updating the parameters of the RR models . Identified disturbance models can be easily incorporated into model- predictive control algorithms.