Model Predicitve Control

Model Predicitve Control

Block diagram with model predictive cotnroller Block diagram with model predictive cotnroller Block diagram with model predictive cotnroller © 2019 IRT

Model predictive control (MPC) is a very successful optimization-based control technique. It consists of repeating the following process online: at each sampling time, solve a finite-horizon optimal control problem and apply the first part of the corresponding optimal solution to the system up to the next sampling time. The main advantages of MPC and reasons for its popularity and widespread success in many application areas are that (i) satisfaction of hard constraints on states and inputs can be guaranteed, (ii) optimization of some performance criterion can directly be incorporated in the controller design, and (iii) it can be applied to general nonlinear systems with possibly multiple inputs.

In our research, we focus on different aspects of current interest in MPC. This includes the development of robust, stochastic, and adaptive MPC schemes for uncertain systems, the consideration of more general control objectives than setpoint/trajectory tracking (economic MPC), the study of distributed MPC schemes for large-scale systems, as well as data- and learning-based MPC methods.


  • J. Köhler, M. A. Müller and F. Allgöwer: Nonlinear reference tracking: An economic model predictive controlperspective.IEEE Transactions on Automatic Control, vol. 64, no. 1, pp. 254-269, 2019.
  • P. Köhler, M. A. Müller and F. Allgöwer:A distributed economic MPC framework for cooperative control under conflicting objectives.Automatica, vol. 96, pp. 368-379, 2018.
  • T. Faulwasser, L. Grüne and M. A. Müller: Economic Nonlinear Model Predictive Control.Foundations and Trends in Systems and Control, vol. 5, no. 1, pp. 1-98, 2018.
  • B. Houska and M. A. Müller: Cost-to-travel functions: a new perspective on optimal and model predictive control. Systems & Control Letters, vol. 106, pp.79-86, 2017.
  • M. A. Müller and K. Worthmann: Quadratic costs do not always work in MPC.Automatica, vol. 82, pp. 269-277, 2017.
  • M. A. Müller and F. Allgöwer: Economic and distributed model prediJ. Köhler, M. A. Müller and F. Allgöwerctive control: recent developments in optimization-based control.SICE Journal of Control, Measurement, and System Integration, vol. 10, no. 2, pp.39-52, March 2017
  • F. A. Bayer, M. Lorenzen, M. A. Müller and F. Allgöwer: Robust Economic Model Predictive Control using Stochastic Information.Automatica, vol. 74, pp. 151-161, 2016.
  • M. A. Müller and L. Grüne: Economic model predictive control without terminal constraints for optimal periodic behavior. Automatica, vol. 70, pp. 128-139, 2016.
  • M. A. Müller, D. Angeli, and F. Allgöwer: On necessity and robustness of dissipativity in economic model predictive control.IEEE Transactions on Automatic Control, vol. 60, no. 6, pp. 1671-1676, 2015.