Universität Stuttgart, Institut für Systemtheorie und Regelungstechnik
Tuesday, January 23, 2018, 11:00
Room 02-012, Georges-Köhler Allee 102, Freiburg 79110, Germany
Model predictive control (MPC) is an optimization-based control technology, which has found successful application in many different industrial fields. It consists of repeatedly solving a finite horizon optimal control problem and then applying the first part of the solution to the considered system.
The main advantages of MPC and the reasons for its widespread success are that (i) satisfaction of hard input and state constraints for the closed-loop system can be guaranteed, (ii) optimization of some performance criterion is directly incorporated in the controller design, and (iii) it can be applied to nonlinear systems with possibly multiple inputs.
In this talk, we focus on some recent developments in the field, so called economic MPC schemes. Here, in contrast to the classical control objective of stabilization, a more general performance criterion is considered which is possibly related to the economics of the considered system. In this case, the optimal operating behavior might not be stationary, but can be more complex (e.g. periodic).
We present conditions that guarantee both closed-loop performance bounds and convergence to the optimal operating behavior. Furthermore, we discuss the development of economic MPC schemes for uncertain systems as well as different relevant aspects for a distributed implementation in the context of large-scale systems.