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 Number | PN4-4 |
Project Name | Theoretical Guarantees for Predictive Control in Adaptive Multi-Agent Scenarios |
Project Duration | July 2019 - December 2022 |
Project Leader | Peter Eberhard Frank Allgöwer |
Project Members | Hannes Eschmann, PhD Researcher |
Project Partners | Carsten Scherer Sebastian Trimpe Kurt Rothermel Oliver Röhrle Syn Schmitt Marc Toussaint |