Control algorithms with suppression of measurable disturbances: Comparison of two methods

Abstract

Many processes are affected by external disturbances caused by the variation of variables that can be measured. This paper compares two control strategies which are suitable for rejection of measurable disturbances. The first method which can successfully handle known measurable disturbances is model predictive control (MPC). Known disturbances can be taken explicitly into account in predictive control. Two different approaches to computation of multi-step-ahead predictions incorporating known measurable disturbances into prediction equations are proposed. The second control algorithm is designed using polynomial theory developed for linear controlled systems. Both methods are based on a same model of a controlled process. Simulation results are also included and quality of control achieved by both methods is compared and discussed.