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151-0660-00L 4 Credits BSC , MSC D-MAVT , D-INFK , D-ITET
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Model Predictive Control

Lecturers & Examiners: Prof. Dr. Melanie Zeilinger
VVZ CR 3.4

Last Updated: 2026-06-03 00:07:54

Abstract

Model predictive control is a flexible paradigm that defines the control law as an optimization problem, enabling the specification of time-domain objectives, high performance control of complex multivariable systems and the ability to explicitly enforce constraints on system behavior. This course provides an introduction to the theory and practice of MPC and covers advanced topics.

Objective

Design and implement Model Predictive Controllers (MPC) for various system classes to provide high performance controllers with desired properties (stability, tracking, robustness,..) for constrained systems.

Content

- Receding-horizon control (MPC) for constrained linear & nonlinear systems - Theoretical properties of MPC: Constraint satisfaction and stability - Relevant optimization theory and algorithms - Computation: Explicit and online MPC - Robust MPC: Robust constraint satisfaction - Extensions to learning-based MPC

Resources

Lecture Notes

Script / lecture notes will be provided.

General Information

Language
English
Levels
BSC , MSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 120 minutes
Aids
Four A4 pages (2 A4 sheets double-sided or 4 A4 sheets single-sided, handwritten or computer typed)

Course Components

Type Title Time & Place Hours
lecture Model Predictive Control No time listed 2 h weekly
exercise Model Predictive Control No time listed 1 h weekly

Offered In