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Found 5 papers, 1 papers with code
LExCI: A Framework for Reinforcement Learning with Embedded Systems
Advances in artificial intelligence (AI) have led to its application in many areas of everyday life.
Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop
The results emphasize the necessity to train RL agents with real hardware, and demonstrate that the maturity of the transferred policies affects both training time and performance, highlighting the strong synergies between TL and XiL simulation.
Introducing a Deep Neural Network-based Model Predictive Control Framework for Rapid Controller Implementation
Model Predictive Control (MPC) provides an optimal control solution based on a cost function while allowing for the implementation of process constraints.
Machine Learning Integrated with Model Predictive Control for Imitative Optimal Control of Compression Ignition Engines
One solution is the use of machine learning (ML) and model predictive control (MPC) to minimize emissions and fuel consumption, without adding substantial computational cost to the engine controller.
Deep Learning based Model Predictive Control for Compression Ignition Engines
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.
