PN4-4

Theoretical Guarantees for Predictive Control in Adaptive Multi-Agent Scenarios

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
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