Model Predictive Control

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.

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
• Practical issues: Tracking and offset-free control of constrained systems, soft constraints
• Robust MPC: Robust constraint satisfaction
• Simulation-based project providing practical experience with MPC

Requirements
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.

Literature
Class notes (will be available online on the Moodle class page).

Exam
Final written exam during the examination session, covers all material. Students are permitted to use two A4 cheat sheets (two sheets = four pages). 

Grading
The final grade is based on an exam and an optional take-home project. The exam takes place during the examination session. The project is a continuous performance assessment (learning task) and requires the student to understand and apply the lecture material.
The grade of the project may contribute 0.25 grade points to the final grade, but only if it helps improving the final grade.

Repetition
The final exam is only offered in the session after the course unit. Repetition is only possible after re-enrolling.