Abstract
The minimum variance (MV) lower bound has been applied to many multivariable control systems in order to assess their performance based on routine operating data. However, such analysis often depends on the selection of a suitable dynamic model of the data and for multivariable systems, there can be many candidate models. Also, uncertainty is often not considered, because standard approximations do not exist for the sampling distribution of these multivariable performance indices. This paper addresses these two issues by using the Bayesian approach to vector autoregression (VAR) modelling with Markov Chain Monte Carlo (MCMC) numerical methods. Dynamic model selection is carried out by using Reversible Jump (RJ) MCMC and it is shown that MCMC can be used to the estimate the non-standard distributions that exist in multivariable MV performance indices. The approach is applied to data from an industrial cross-directional (CD) control system by using a more general class of model than has previously been studied for these systems.
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