EE60036: Model Predictive Control

Welcome to this PG level subject.

Course Content (Tentative)

Model Predictive Control (MPC) is a general framework for optimization-based control of constrained dynamical systems. It has several advantages over classical (Dynamic Programming based) optimal control approaches, including handling constraints on states and input, computational tractability and guarantees on closed-loop stability. Deployment of MPC requires efficiently solving an optimization problem in real-time. This course aims to introduce the theory, computation and application of MPC.

Here is a basic overview of the topics that are planned to be covered.

  1. Introduction to Convex Optimization (2 Weeks)
  2. Optimal Control and MPC for Linear Systems (2 Weeks)
  3. Recursive Feasibility and Closed Loop Stability (1 week)
  4. Nonlinear and Economic MPC (1 week)
  5. Output Feedback and Moving Horizon Estimation (1 Week)
  6. Robust, Stochastic and Data-Driven MPC (4 Weeks)
  7. MPC for Hybrid Systems (1-2 Weeks)
  8. Computation: Algorithms and Explicit Control Laws (1-2 Weeks)


This course will have a strong computational component. We will implement different MPC schemes using MATLAB. The students will need to do a project in the second half ideally on a topic related to their field of research. The following optimization package will be used.

  1. YALMIP.

Primary References

The lectures will borrow materials from the following references.

  1. Predictive Control for Linear and Hybrid Systems, by Francesco Borrelli, Alberto Bemporad, and Manfred Morari, Cambridge University Press, 2017
  2. MPC Lectures by Alberto Bemporad.
  3. Handbook of Model Predictive Control, Springer-Birkhauser, 2019.
  4. Model Predictive Control: Theory, Computation and Design by James B. Rawlings, David Q. Mayne and Moritz M. Diehl. 2nd Edition.

Selected Research Papers

The following is a selection of seminal papers that developed the theory, computation and showed their applications in many domains.

  1. Mayne, D.Q., Rawlings, J.B., Rao, C.V. and Scokaert, P.O., 2000. Constrained model predictive control: Stability and optimality. Automatica, 36(6), pp.789-814.
  2. Bemporad, A. and Morari, M., 1999. Control of systems integrating logic, dynamics, and constraints. Automatica, 35(3), pp.407-427.
  3. Bemporad, A., Morari, M., Dua, V. and Pistikopoulos, E.N., 2002. The explicit linear quadratic regulator for constrained systems. Automatica, 38(1), pp.3-20.
  4. Rao, C.V., Rawlings, J.B. and Lee, J.H., 2001. Constrained linear state estimation—a moving horizon approach. Automatica, 37(10), pp.1619-1628.
  5. Langson, W., Chryssochoos, I., Raković, S.V. and Mayne, D.Q., 2004. Robust model predictive control using tubes. Automatica, 40(1), pp.125-133.
  6. Goulart, P.J., Kerrigan, E.C. and Maciejowski, J.M., 2006. Optimization over state feedback policies for robust control with constraints. Automatica, 42(4), pp.523-533.
  7. Schildbach, G., Fagiano, L., Frei, C. and Morari, M., 2014. The scenario approach for stochastic model predictive control with bounds on closed-loop constraint violations. Automatica, 50(12), pp.3009-3018.
  8. Mesbah, A., 2016. Stochastic model predictive control: An overview and perspectives for future research. IEEE Control Systems Magazine, 36(6), pp.30-44.
  9. Berberich, J., Köhler, J., Muller, M.A. and Allgower, F., 2020. Data-driven model predictive control with stability and robustness guarantees. IEEE Transactions on Automatic Control.
  10. Vazquez, S., Rodriguez, J., Rivera, M., Franquelo, L.G. and Norambuena, M., 2016. Model predictive control for power converters and drives: Advances and trends. IEEE Transactions on Industrial Electronics, 64(2), pp.935-947.
  11. Di Cairano, S., Bernardini, D., Bemporad, A. and Kolmanovsky, I.V., 2013. Stochastic MPC with learning for driver-predictive vehicle control and its application to HEV energy management. IEEE Transactions on Control Systems Technology, 22(3), pp.1018-1031.
  12. Huang, Y., Wang, H., Khajepour, A., He, H. and Ji, J., 2017. Model predictive control power management strategies for HEVs: A review. Journal of Power Sources, 341, pp.91-106.
  13. Parisio, A., Rikos, E. and Glielmo, L., 2014. A model predictive control approach to microgrid operation optimization. IEEE Transactions on Control Systems Technology, 22(5), pp.1813-1827.
  14. Oldewurtel, F., Parisio, A., Jones, C.N., Gyalistras, D., Gwerder, M., Stauch, V., Lehmann, B. and Morari, M., 2012. Use of model predictive control and weather forecasts for energy efficient building climate control. Energy and Buildings, 45, pp.15-27.