Nonlinear Predictive Control and Moving Horizon Estimation : An Introductory Overview

In the past decade model predictive control (MPC) has become a preferred control strategy for a large number of processes. The main reasons for this preference include the ability to handle constraints in an optimal way and the flexible formulation in the time domain. Linear MPC schemes, i.e. MPC schemes for which the prediction is based on a linear description of the plant, are by now routinely used in a number of industrial sectors and the underlying control theoretic problems, like stability, are well studied. Nonlinear model predictive control (NMPC), i.e. MPC based on a nonlinear plant description, has only emerged in the past decade and the number of reported industrial applications is still fairly low. Because of its additional ability to take process nonlinearities into account, expectations on this control methodology are high.

In this article we give an introduction and an overview of the field of MPC with a special emphasis on nonlinear model predictive control. Also a fresh look on the fairly new area of moving horizon estimation, which is the dual of MPC, is given. For both areas the problem formulation, system theoretical properties and computational aspects are discussed together with condensed accounts of their respective histories and issues pertaining to their industrial application.