Stochastic and Hybrid Model Predictive Control

Alberto Bemporad

Stuttgart, April 23-27, 2012


The course is intended for students and engineers who want to learn the theory and practice of Model Predictive Control (MPC) and optimization of constrained linear, stochastic, and hybrid dynamical systems. MPC has a well-established tradition in the process industries since a few decades for controlling large multivariable systems subject to various constraints on manipulated variables and controlled outputs. In recent years, MPC is also rapidly expanding in several other domains, such as in the automo- tive and aerospace industries and in smart energy grids. The theoretical foundations of MPC concern stability and robustness guarantees, and how to deal with hybrid and switched dynamics, fast-sampling processes, and stochastic uncertainty. The course will make use of the MPC Toolbox for MATLAB developed by the speaker and co-workers (distributed by The MathWorks, Inc.) for basic linear MPC, and on the Hybrid Toolbox for explicit and hybrid MPC.

Lecture notes

1. Linear MPC
3. Explicit linear MPC
4. Hybrid dynamical models for MPC
5. Hybrid MPC
6. Examples of hybrid MPC
7. Stochastic MPC
8. Conclusions


Model Predictive Control Toolbox for MATLAB
Hybrid Toolbox for MATLAB


List of publications