151-0660-00L  Model Predictive Control

SemesterSpring Semester 2017
LecturersM. Zeilinger
Periodicityyearly course
Language of instructionEnglish



Catalogue data

AbstractModel 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.
ObjectiveDesign 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 notesScript / lecture notes will be provided.
Prerequisites / NoticeOne 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 credits4 credits
ExaminersM. Zeilinger
Typesession examination
Language of examinationEnglish
Course attendance confirmation requiredNo
RepetitionThe performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examinationwritten 120 minutes
Additional information on mode of examinationThe 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 aidsTwo 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 linkCourse webpage
RecordingRecordings from the previous block course (accessible to ETH students only)
Only public learning materials are listed.

Courses

NumberTitleHoursLecturers
151-0660-00 VModel Predictive Control2 hrs
Thu09-11HG D 1.2 »
M. Zeilinger
151-0660-00 UModel Predictive Control1 hrs
Thu11-12HG D 1.2 »
M. Zeilinger

Restrictions

There are no additional restrictions for the registration.

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