Výsledky vyhledávání - "Andert, Jakob"

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LExCI: A Framework for Reinforcement Learning with Embedded Systems

Autor: Badalian, Kevin, Koch, Lucas, Brinkmann, Tobias, Picerno, Mario, Wegener, Marius, Lee, Sung-Yong, Andert, Jakob

Publikováno v: Applied Intelligence (2024)

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 particularly promising approach as it is centred around the idea of a

Externí odkaz: http://arxiv.org/abs/2312.02739

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Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop

Autor: Picerno, Mario, Koch, Lucas, Badalian, Kevin, Wegener, Marius, Schaub, Joschka, Koch, Charles Robert, Andert, Jakob

The process of developing control functions for embedded systems is resource-, time-, and data-intensive, often resulting in sub-optimal cost and solutions approaches. Reinforcement Learning (RL) has great potential for autonomously training agents t

Externí odkaz: http://arxiv.org/abs/2310.17671

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Introducing a Deep Neural Network-based Model Predictive Control Framework for Rapid Controller Implementation

Autor: Gordon, David C., Winkler, Alexander, Bedei, Julian, Schaber, Patrick, Andert, Jakob, Koch, Charles R.

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 control technique, the performance of MPC strongly depends on the mode

Externí odkaz: http://arxiv.org/abs/2310.08392

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Machine Learning Integrated with Model Predictive Control for Imitative Optimal Control of Compression Ignition Engines

Autor: Norouzi, Armin, Shahpouri, Saeid, Gordon, David, Winkler, Alexander, Nuss, Eugen, Abel, Dirk, Andert, Jakob, Shahbakhti, Mahdi, Koch, Charles Robert

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 emissions is needed. One solution is the use of machine learning (ML) a

Externí odkaz: http://arxiv.org/abs/2204.00142

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Deep Learning based Model Predictive Control for Compression Ignition Engines

Autor: Norouzi, Armin, Shahpouri, Saeid, Gordon, David, Winkler, Alexander, Nuss, Eugen, Abel, Dirk, Andert, Jakob, Shahbakhti, Mahdi, Koch, Charles Robert

Machine learning (ML) and a nonlinear model predictive controller (NMPC) are used in this paper to minimize the emissions and fuel consumption of a compression ignition engine. In this work machine learning is applied in two methods. In the first app

Externí odkaz: http://arxiv.org/abs/2204.00139

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