PREDICTIVE CONTROLLER-DESIGN BY PRINCIPAL COMPONENTS-ANALYSIS

A new method of designing predictive controllers has been developed that is based on a singular value analysis of the process dynamics. The primary design parameter in the new method is the number of principal components of the system generalized inverse that are retained in the approximate process inverse used by the controller. The effects of retaining each of the individual components on closed-loop performance and robustness can be easily calculated. Choices of other controller design parameters have a minimal impact on the results of the new method. A particular feature is that explicit move suppression is not required. The method works particularly well on multiple-input/multiple-output (MIMO) processes and tolerates changes in process scaling and output weighting. Application of the method is illustrated wth a simple single-input/ single-output (SISO) process model and with two MIMO distillation column models.