Reinforcement learning applied to dilute combustion control for increased fuel efficiency
Bryan P Maldonado,
Brian C Kaul,
Catherine D Schuman
et al.
Abstract:To reduce the modeling burden for control of spark-ignition engines, reinforcement learning (RL) has been applied to solve the dilute combustion limit problem. Q-learning was used to identify an optimal control policy to adjust the fuel injection quantity in each combustion cycle. A physics-based model was used to determine the relevant states of the system used for training the control policy in a data-efficient manner. The cost function was chosen such that high cycle-to-cycle variability (CCV) at the dilute… Show more
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