Reduction and identification of multivariable processes using regression analysis

The paper is concerned with the application of multivariate regression analysis to the reduction of multivariable control problems and to the identification of linear and nonlinear time-varying processes. Reduction is performed by grouping the input and output variables of a many variable process into a small number of groups. Control is exercised in terms of a few variables, each representing such a group. Regression is further applied to the dynamic identification of reduced or unreduced linear and nonlinear multivariable processes where no a priori information of the dynamic characteristics is available. Both reduction and identification may be performed on-line. The resulting techniques are conveniently incorporated in control procedures based on dynamic programming and on predictive adaptation.