The quasi-infinite horizon approach to nonlinear model predictive control

Abstract

In the past decade nonlinear model predictive control (NMPC) has witnessed steadily increasing attention from control theoretists as well as control practitioners. The practical interest is driven by the fact that today’s processes need to be operated under tighter performance specifications. At the same time more and more constraints, stemming for example from environmental and safety considerations, need to be satisfied. Often these demands can only be met when process nonlinearities and constraints are explicitly considered in the controller. This paper reviews one NMPC technique, often referred to as quasi-infinite horizon NMPC (QIH-NMPC). An appealing feature of QIH-NMPC is the fact that a short control horizon can be realized, implying reduced computational load. At the same time the controller achieves favorable properties such as stability and performance. After introducing the basic concept of model predictive control, the key ideas behind QIH-NMPC are discussed and the resulting properties for the state feedback case are presented. Additionally some new results on output feedback NMPC using high-gain observers are given. With the use of a realistic process control example, it is demonstrated, that even fairly large problems can be considered using NMPC techniques if state-of-the-art optimization techniques are combined with efficient NMPC schemes.