13 citations · 29 across the 4 of their papers we have counts for
3 papers · 1 filter
LExCI: A Framework for Reinforcement Learning with Embedded Systems
Kevin Badalian, Lucas Koch, Tobias Brinkmann +4
Advances in artificial intelligence (AI) have led to its application in many areas of everyday life. In the context of control engineering, reinforcement learning (RL) represents a…
Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop
Mario Picerno, Lucas Koch, Kevin Badalian +4
The process of developing control functions for embedded systems is resource-, time-, and data-intensive, often resulting in sub-optimal cost and solutions approaches. Reinforcemen…
Introducing a Deep Neural Network-based Model Predictive Control Framework for Rapid Controller Implementation
David C. Gordon, Alexander Winkler, Julian Bedei +3
Model Predictive Control (MPC) provides an optimal control solution based on a cost function while allowing for the implementation of process constraints. As a model-based optimal…