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151-0660-00L
Model Predictive Control

Professor(en):
M.N. Zeilinger
Betreuer:
Vorlesung:
Link zum Kurskatalog
Spring 2017
Webseite:
Ziele:
Description
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.
Vorlesungslevel:
D-ITET Master, Systems and Control specialization
Recommended Core Courses
Voraussetzungen:
One semester course on automatic control, Matlab, linear algebra.
Important concepts to start the course: State-space modeling, basic concepts of stability, linear quadratic regulation / unconstrained optimal control.
Inhalt:
Tentative 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


More information: http://www.idsc.ethz.ch/education/lectures/model-predictive-control.html
Dokumentation: