13 citations · 29 across the 4 of their papers we have counts for
10 papers
Deep Behaviour Cloning of Model Predictive Control for Real-Time Operation of a Hydrogen-Diesel Dual-Fuel Engine
Alexander Winkler, Neeraj Naduvath Mana, David Gordon +1
Hydrogen-diesel dual-fuel (H2DF) combustion engines offer a promising pathway for decarbonising hard-to-electrify transport sectors, yet their highly nonlinear dynamics and coupled…
Hydrogen-Diesel Dual-Fuel Engine Operation Using Real-Time GRU-Based Nonlinear Model Predictive Control
Alexander Winkler, Vasu Sharma, Julian Bedei +4
Hydrogen-diesel dual-fuel (H2DF) combustion reduces combustion-out CO2 emissions but exhibits nonlinear cycle-to-cycle dynamics at high hydrogen energy shares (HES). This work eval…
Hybrid Reinforcement Learning and Model Predictive Control for Adaptive Control of Hydrogen-Diesel Dual-Fuel Combustion
Julian Bedei, Murray McBain, Alexander Winkler +3
Reinforcement Learning (RL) and Machine Learning Integrated Model Predictive Control (ML-MPC) are promising approaches for optimizing hydrogen-diesel dual-fuel engine control, as t…
Incorporating a Deep Neural Network into Moving Horizon Estimation for Embedded Thermal Torque Derating of an Electric Machine
Alexander Winkler, Pranav Shah, Katrin Baumgärtner +3
This study presents a novel state estimation approach integrating Deep Neural Networks (DNNs) into Moving Horizon Estimation (MHE). This is a shift from using traditional physics-b…
Safe Reinforcement Learning-based Control for Hydrogen Diesel Dual-Fuel Engines
Vasu Sharma, Alexander Winkler, Armin Norouzi +3
The urgent energy transition requirements towards a sustainable future stretch across various industries and are a significant challenge facing humanity. Hydrogen promises a clean,…
Safe Reinforcement Learning for Real-World Engine Control
Julian Bedei, Lucas Koch, Kevin Badalian +3
This work introduces a toolchain for applying Reinforcement Learning (RL), specifically the Deep Deterministic Policy Gradient (DDPG) algorithm, in safety-critical real-world envir…
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…
Machine Learning Integrated with Model Predictive Control for Imitative Optimal Control of Compression Ignition Engines
Armin Norouzi, Saeid Shahpouri, David Gordon +6
The high thermal efficiency and reliability of the compression-ignition engine makes it the first choice for many applications. For this to continue, a reduction of the pollutant e…