151-0660-00L  Model Predictive Control

Semester Spring Semester 2017
Lecturers M. Zeilinger
Periodicity yearly course
Language of instruction English



Catalogue data

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 - Review of required optimal control theory
- Basics on optimization
- Receding-horizon control (MPC) for constrained linear systems
- Theoretical properties of MPC: Constraint satisfaction and stability
- Computation: Explicit and online MPC
- Practical issues: Tracking and offset-free control of constrained systems, soft constraints
- Robust MPC: Robust constraint satisfaction
- Nonlinear MPC: Theory and computation
- Hybrid MPC: Modeling hybrid systems and logic, mixed-integer optimization
- Simulation-based project providing practical experience with MPC
Lecture notes Script / lecture notes will be provided.
Prerequisites / Notice One semester course on automatic control, Matlab, linear algebra.
Courses on signals and systems and system modeling are recommended. Important concepts to start the course: State-space modeling, basic concepts of stability, linear quadratic regulation / unconstrained optimal control.

Expected student activities: Participation in lectures, exercises and course project; homework (~2hrs/week).

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits 4 credits
Examiners M. Zeilinger
Type session examination
Language of examination English
Course attendance confirmation required No
Repetition The performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examination written 120 minutes
Additional information on mode of examination The final grade is based on the session exam, an optional in-class quiz, and optional programming exercises: The grade of the quiz may contribute 15% to the final grade, but only if it helps improving the final grade. The average grade of the programming exercises may contribute 15% to the final grade, but only if it helps improving the final grade.
Written aids Two A4 sheets of paper (4 pages, handwritten or computer typed)
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

 
Main link Course webpage
Recording Recordings from the previous block course (accessible to ETH students only)
Only public learning materials are listed.

Courses

Number Title Hours Lecturers
151-0660-00 V Model Predictive Control 2 hrs
Thu 09-11 HG D 1.2 »
M. Zeilinger
151-0660-00 U Model Predictive Control 1 hrs
Thu 11-12 HG D 1.2 »
M. Zeilinger

Restrictions

There are no additional restrictions for the registration.

Offered in

Programme Section Type
Doctoral Dep. of Information Technology and Electrical Engineering Doctoral and Post-Doctoral Courses W Information
Electrical Engineering and Information Technology Master Core Subjects W Information
Electrical Engineering and Information Technology Master Recommended Subjects W Information
Integrated Building Systems Master Specialised Courses W Information
Mechanical Engineering Master Robotics, Systems and Control W Information
Mathematics Master Control and Automation W Information
Computational Science and Engineering Bachelor Electives W Information
Computational Science and Engineering Master Electives W Information
Robotics, Systems and Control Master Core Courses W Information