Advanced Predictive Control (APC/APreC)
Many modern control tasks involve multiple interacting variables, challenging objectives, and constraints that cannot be handled efficiently by classical control methods. Examples include resource-aware operation, actuator limitations, safety constraints, or the need to coordinate several subsystems simultaneously. Predictive control provides a powerful framework to address these challenges by formulating the control task as an optimal control problem (OCP), which incorporates system dynamics, objectives, and constraints explicitly. This OCP is solved repeatedly over a receding horizon, enabling high performance, resource efficiency, and constraint satisfaction in closed-loop operation. While model predictive control (MPC) for linear systems is established, advanced methods are required to deal with nonlinearities, coupled objectives, uncertainties, disturbances, or the absence of accurate models.
Organizational Info (Winter term 2026/27)
| Lectures | Exercises | |
|---|---|---|
| Start | 14.10.2026 | 20.10.2026 |
| Time | Wednesdays, 10:15 to 11:45 | Tuesdays, 14:15 to 15:45 |
| Room | MB E21/E22 | MB E21/E22 |
| Lecturers / Tutors | Moritz Schulze Darup | Manuel Klädtke and Philipp Binfet |
| Moodle | https://moodle.tu-dortmund.de/course/view.php?id=59959 | |
| Language | English (or German depending on audience) | |
Content (according to module description)
Building on a brief review of classical MPC, this course introduces advanced concepts of predictive control. In particular, the following topics are covered:
- Review of classical MPC for linear systems,
- Data-driven predictive control (DPC),
- Robust and stochastic MPC for uncertain and disturbed systems,
- Nonlinear model predictive control (NMPC),
- Distributed and cooperative MPC for multi-agent systems,
- Implementation aspects and illustrative applications.
Learning objectives and Competencies
Upon completion of the course, students are able to
- identify and characterize application scenarios for advanced predictive control schemes,
- formulate OCPs based on application-specific models, constraints, and objectives, and solve them numerically using appropriate software,
- evaluate predictive control approaches through numerical experiments, compare different methods, and select a suitable control scheme for a given task.
Literature
J. B. Rawlings, D. Q. Mayne, and M. M. Diehl. Model Predictive Control: Theory, Computation, and Design. Nob Hill Publishing, 2nd Edition, 2017.
B. Kouvaritakis and M. Cannon. Model Predictive Control: Classical, Robust and Stochastic. Springer, 2016.
Lars Grüne and Jürgen Pannek. Nonlinear Model Predictive Control: Theory and Algorithms. Springer, 2nd Edition, 2017.