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 is the number of principal components of the system generalized inverse to retain in the approximate process inverse used by the controller. The effects 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. Explicit move suppression is not required. The method works particularly well on MIMO processes and tolerates changes in process scaling and output weighting. Application of the method to two distillation column models is illustrated.