Algorithms with state estimation in linear and nonlinear model predictive control

Abstract Model predictive control (MPC) algorithms with state-space process modelling, both linear and nonlinear, and state estimation methods for this algorithms are the subject of this paper. The considerations are under realistic assumption that processes are under influence of external disturbances and their models are not precise. This leads in most cases to errors in state estimation and, further, may lead to errors in feedback control. Attention has been paid to this problem in recent years. Two approaches are now available, an earlier one with additional disturbance state modelling and process-and-disturbance state estimation and a more recently proposed approach with different way of disturbance modelling and estimation of the process state only. The main aim of this paper is to provide a comprehensive comparison of the two mentioned approaches, including also discussion of available state estimation algorithms. To the best knowledge of the authors, there is still a lack of clear understanding of the differences between the mentioned approaches, in particular from practical point of view. After short presentation of the two methods and analysis of their theoretical aspects, a comprehensive comparative analysis is provided on a nonlinear example of the polymerization reactor.