7 citations · 7 across the 3 of their papers we have counts for
3 papers
Fine-Tuning of Neural Network Approximate MPC without Retraining via Bayesian Optimization
Henrik Hose, Paul Brunzema, Alexander von Rohr +3
Approximate model-predictive control (AMPC) aims to imitate an MPC's behavior with a neural network, removing the need to solve an expensive optimization problem at runtime. Howeve…
RockNet: Distributed Learning on Ultra-Low-Power Devices
Alexander Gräfe, Fabian Mager, Marco Zimmerling +1
As Machine Learning (ML) becomes integral to Cyber-Physical Systems (CPS), there is growing interest in shifting training from traditional cloud-based to on-device processing (Tiny…
DMPC-Swarm: Distributed Model Predictive Control on Nano UAV Swarms
Alexander Gräfe, Joram Eickhoff, Marco Zimmerling +1
Swarms of unmanned aerial vehicles (UAVs) are increasingly becoming vital to our society, undertaking tasks such as search and rescue, surveillance and delivery. A special variant…