Model Predictive Control

Course Overview

Model Predictive Control (MPC) is a general framework for optimization-based control of constrained dynamical systems. It offers several advantages over classical optimal control approaches, including explicit constraint handling, computational tractability, and closed-loop stability guarantees. This course covers the theory, computation, and application of MPC.

Topics Covered

1. Introduction to Convex Optimization2 weeks
2. Optimal Control and MPC for Linear Systems2 weeks
3. Recursive Feasibility and Closed-Loop Stability1 week
4. Nonlinear and Economic MPC1 week
5. Output Feedback and Moving Horizon Estimation1 week
6. Robust, Stochastic and Data-Driven MPC4 weeks
7. MPC for Hybrid Systems1–2 weeks
8. Computation: Algorithms and Explicit Control Laws1–2 weeks

Computation

This course has a strong computational component. Different MPC schemes are implemented in MATLAB. Students complete a project in the second half, ideally on a topic related to their own research. The primary optimization package used is YALMIP.

Primary References

1. Model Predictive Control: Classical, Robust and Stochastic by B. Kouvaritakis and M. Cannon. Springer Cham, 2016. [Link]
2. Predictive Control for Linear and Hybrid Systems by F. Borrelli, A. Bemporad and M. Morari. Cambridge University Press, 2017. [Link]
3. MPC Lecture Notes by A. Bemporad. [Link]
4. Handbook of Model Predictive Control. Springer-Birkhauser, 2019. [Link]
5. Model Predictive Control: Theory, Computation and Design by J. B. Rawlings, D. Q. Mayne and M. M. Diehl. 2nd Edition. [Link]

Selected Research Papers

1. Mayne, Rawlings, Rao and Scokaert (2000). Constrained model predictive control: Stability and optimality. Automatica, 36(6), pp.789–814.
2. Bemporad and Morari (1999). Control of systems integrating logic, dynamics, and constraints. Automatica, 35(3), pp.407–427.
3. Langson, Chryssochoos, Raković and Mayne (2004). Robust model predictive control using tubes. Automatica, 40(1), pp.125–133.
4. Goulart, Kerrigan and Maciejowski (2006). Optimization over state feedback policies for robust control with constraints. Automatica, 42(4), pp.523–533.
5. Lorenzen, Dabbene, Tempo and Allgöwer (2016). Constraint-tightening and stability in stochastic MPC. IEEE Trans. Autom. Control.
6. Berberich, Köhler, Muller and Allgower (2020). Data-driven model predictive control with stability and robustness guarantees. IEEE Trans. Autom. Control.