Model Predictive Control
Towards Frontier Applications in Learning and Control
📍Uppsala University, Ångströmlaboratoriet
🗓️ October - November 2024
This PhD course explores the fundamental ideas and principles of Model Predictive Control, nowadays regarded as one of the most popular and powerful control strategies. We aim to familiarize students with the theoretical and mathematical foundations on which MPC is built, its strengths and weaknesses, as well as its computational aspects. This will serve as a starting point for exploring the many variants of MPC, including data-driven and reinforcement learning-based ones.
By taking this course the student will learn to:
explain the fundamental principles of model predictive control;
autonomously explore the tangle of MPC approaches, choosing the one best suited to the specific situation;
implement an MPC controller to solve a challenging control problem.
Tentative course schedule
Lecture 1 - Oct. 8, 2024, 13:15 to 15:00 - Introduction to the course
Lecture 2 - Oct. 17, 2024 13:15 to 15:00 - Fundamentals of MPC
Lecture 3 - Oct. 24, 2024, 13:15 to 15:00 - Principles of Optimization for MPC
Lecture 4 - Oct. 31, 2024, 13:15 to 15:00 - Stability and robustness of MPC
Lecture 5 - Nov. 7, 2024, 13:15 to 15:00 - Implementing MPC: software tools and computational aspects
Lecture 6 - Nov. 14, 2024, 13:15 to 15:00 - Nonlinear MPC
Lecture 7 - Nov. 21, 2024, 13:15 to 15:00 - Data-driven MPC
Lecture 8 - Nov. 28, 2024, 13:15 to 15:00 - MPC and Reinforcement Learning
Lecture 9 - End of January, 2025 - Final presentations
Proactive attendance of at least 6 out of the first 8 lectures is required. Attending Lecture 9 is, in general, mandatory.
Examination
Students will undertake a course project in groups (2-3 people/group), where they get to apply MPC to a real-world problem they deem interesting. The output of this project will be
A short technical report of their project and reproducible code.
A 15-minutes presentation of their project to their colleagues.
Recommended prerequesites
Familiarity with linear algebra, differential equations, and optimization
Programming experience with MATLAB, Python, or Julia
Basic knowledge of control theory (e.g., Automatic Control I-II)
Teachers
Per Mattsson, Associate Professor at the Uppsala University
Fabio Bonassi, Postdoc at the Uppsala University
Daniel Arnström, Postdoc at the Uppsala University
Registration
Master's or PhD students are welcome to attend the course. The course is free of charge.
Registration deadline: September 30
Questions?
Contact Fabio Bonassi.