Application of regression analysis to reduction of multivariable control problems and to process identification

The paper is concerned with the application of multivariate regression analysis to the reduction of a many-variable control problem 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 of variables. Control is exercised in terms of a few variables, each representing such a group. Regression is further applied to the identification of linear and nonlinear multivariable processes where no apriori information of the dynamic characteristics is available. The resulting identification subroutines are conveniently incorporated in control procedures based on predictive-adaptive control and on dynamic programming.