MIMO Process Identification: A Markov Parameter Approach

Abstract

In the previous two chapters, we have studied the LS method and different extensions of the LS method; most of the extensions belong to the family of the prediction error methods. We have seen that the properties of the LS method and those of the prediction error methods are complementary: the LS method is numerically simple and reliable, but model quality is low due to the bias of the estimate; the prediction error methods can give accurate estimates, but the algorithms are numerically difficult. Logically, one may think of combining the advantages of the LS method and the prediction error methods in some way. Moreover, there are common shortcomings of these methods: for example, it is difficult to determine model structure for a MIMO process. In this chapter we shall present an identification method which is suitable for MIMO process identification and numerically reliable. The method is developed by Backx and coworkers (see Backx, 1987, Backx and Damen, 1989).