Project description
Motivation
Bridging the gap between theoretic research in distributed and learning-based model predictive control and multi-agent applications
Goals
- Modularity – local modification of robotic networks, including learning of unmodeled parts of the system dynamics based on communicated and sensed data (learning from neighbors and the environment)
- Decoupling design processes of the local controllers and the global coordination scheme
- Taking into account the specific dynamic properties of practical real-world robots
- Show practical applicability in realistic simulative and experimental scenarios, e.g. in robotics applications
Methods
- combination of distributed and learning-based model predictive control
- Gaussian process (GP) regression to infer unmodeled parts of the system dynamics at runtime
- continued development of distributed control hardware test benches, producing real-world data
Project information
Project title | Theoretical guarantees for predictive control in adaptive multi-agent scenarios |
Project leaders | Peter Eberhard (Frank Allgöwer) |
Project partners |
Carsten Scherer |
Project duration | July 2019 - December 2022 |
Project number | PN 4-4 |
- Follow-up project 4-4 (II)
Learning from data - predictive control in adaptive multi-agent scenarios