Techniques for predictor design in multivariable predictive control

Abstract

Model Based Predictive Control (MBPC) or only Predictive Control is one of the control methods which have developed considerably over a few past years. It is mostly based on discrete models of controlled systems. Model of a controlled system is used for computation of predictions of the systems output on the basis of past inputs, outputs and states and designed sequence of future control increments. This paper is focused in comparison of various approaches to computation of multi-step-ahead predictions using a multivariable input - output model. Computational aspects of derivation of predictions can be limiting especially in adaptive predictive control. Many processes are affected by external disturbances that can be measured. Inclusion of measurable disturbances into prediction equations for different approaches was also elaborated.