New methods for robust MPC with high-dimensional uncertainty
| Subject area | Automation, Control Systems, Robotics, Mechatronics, Cyber Physical Systems |
| Term | since 2019 |
| Funding | Deutsche Forschungsgemeinschaft (DFG) |
Project description
Model predictive control (MPC) is a popular optimization-based control scheme that has become the standard advanced control strategy in many different fields.The computation time needed to solve the optimization problems that result from standard MPC formulations is in most cases not the main obstacle for the widespread adoption of MPC. The necessity of a good model is, however, still a major challenge. Even when models are available or can be derived from first principles, determining the value of all the necessary parameters can be extremely difficult. As a result, uncertainty is always present in real-world scenarios, either in the form of plant-model mismatch or due to the presence of disturbances which can lead to important performance degradation of MPC or even to instability of the closed-loop system.Several robust MPC approaches have been proposed in recent years to deal with uncertainty in the framework of MPC. Most of the available robust MPC methods have been demonstrated only for small systems with a few uncertainties because the approaches are difficult to design, result in intractable optimization problems or are very conservative.Enabling a systematic and reliable application of robust MPC schemes to considerably larger systems that are potentially affected by many uncertainties is a necessary step to significantly increase the impact of MPC approaches and is the main motivation of this project.Rather than focusing on the efficient numerical solutions of the large optimization problems that result from the formulation of most robust MPC schemes, the central goal of this project is to develop novel methods that lead to tractable problems by using the following three ideas i) exploiting the system properties, ii) considering randomized optimization methods, iii) by moving computations to an offline analysis as much as possible leveraging the possibilities of data-based methods to learn robust control policies. Different methods related to these three ideas will be developed taking advantage of recent advances on the multi-stage MPC approach, which models the possible evolution of the uncertainty using a tree of discrete scenarios. We expect to obtain both basic scientific insights – like new methods to analyze, design and adapt robust MPC with strict theoretical guarantees, as well as applicable approximate schemes that can attain robust performance in real-world systems.
Researchers
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Location & approach
The campus of TU Dortmund University is located close to interstate junction Dortmund West, where the Sauerlandlinie A 45 (Frankfurt-Dortmund) crosses the Ruhrschnellweg B 1 / A 40. The best interstate exit to take from A 45 is “Dortmund-Eichlinghofen” (closer to South Campus), and from B 1 / A 40 “Dortmund-Dorstfeld” (closer to North Campus). Signs for the university are located at both exits. Also, there is a new exit before you pass over the B 1-bridge leading into Dortmund.
To get from North Campus to South Campus by car, there is the connection via Vogelpothsweg/Baroper Straße. We recommend you leave your car on one of the parking lots at North Campus and use the H-Bahn (suspended monorail system), which conveniently connects the two campuses.
The Laboratory of Process Automation Systems is located at Building G2 on the North Campus. Find more information here.
TU Dortmund University has its own train station (“Dortmund Universität”). From there, suburban trains (S-Bahn) leave for Dortmund main station (“Dortmund Hauptbahnhof”) and Düsseldorf main station via the “Düsseldorf Airport Train Station” (take S-Bahn number 1, which leaves every 15 or 30 minutes). The university is easily reached from Bochum, Essen, Mülheim an der Ruhr and Duisburg.
You can also take the bus or subway train from Dortmund city to the university: From Dortmund main station, you can take any train bound for the Station “Stadtgarten”, usually lines U41, U45, U 47 and U49. At “Stadtgarten” you switch trains and get on line U42 towards “Hombruch”. Look out for the Station “An der Palmweide”. From the bus stop just across the road, busses bound for TU Dortmund University leave every ten minutes (445, 447 and 462). Another option is to take the subway routes U41, U45, U47 and U49 from Dortmund main station to the stop “Dortmund Kampstraße”. From there, take U43 or U44 to the stop “Dortmund Wittener Straße”. Switch to bus line 447 and get off at “Dortmund Universität S”.
The Laboratory of Process Automation Systems is located at Building G2 on the North Campus. Find more information here.
The H-Bahn is one of the hallmarks of TU Dortmund University. There are two stations on North Campus. One (“Dortmund Universität S”) is directly located at the suburban train stop, which connects the university directly with the city of Dortmund and the rest of the Ruhr Area. Also from this station, there are connections to the “Technologiepark” and (via South Campus) Eichlinghofen. The other station is located at the dining hall at North Campus and offers a direct connection to South Campus every five minutes.
The Laboratory of Process Automation Systems is located at Building G2 on the North Campus. Find more information here. The building is within 5min walking distance of the H-Bahn Station "Dining Hall at North Campus".
The facilities of TU Dortmund University are spread over two campuses, the larger Campus North and the smaller Campus South. Additionally, some areas of the university are located in the adjacent “Technologiepark”.
Site Map of TU Dortmund University (Second Page in English).
Interactive map
The facilities of TU Dortmund University are spread over two campuses, the larger Campus North and the smaller Campus South. Additionally, some areas of the university are located in the adjacent "Technologiepark".
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