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:

• 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
  

Requirements
One semester course on automatic control, linear algebra required.
Note: A basic introduction to MPC (e.g. Control Systems II or Computational Control) is recommended.
Important concepts to start the course: State-space modeling, basic concepts of stability, linear quadratic regulation / unconstrained optimal control, MPC for linear systems.

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

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

Grading
The final grade is based on an exam. The exam takes place during the examination session. 

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