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  • https://doi.org/10.1049/icp.2025.3571Copy DOI Icon

A reinforcement learning-based method for pressure distribution control in engine combustion chambers

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Abstract

Controlling the combustion process in aero-engines is critical for optimizing performance, yet it presents significant challenges due to complex, nonlinear dynamics. Traditional Proportional-Integral-Derivative (PID) controllers are often inadequate for simultaneously managing the multiple interdependent parameters required for efficient combustion. This paper introduces a novel control method based on the Soft Actor-Critic (SAC) deep reinforcement learning algorithm to regulate pressure distribution within an engine combustion chamber. The proposed model synergistically controls key parameters—fuel equivalence ratio, fuel injection port flow proportion, and slider displacement—to achieve adaptive and stable combustion management. Experiments in a dedicated simulation environment demonstrate that the method successfully achieves high-precision pressure tracking, maintaining a root mean squared error (RMSE) of less than 1%. This approach offers a robust and efficient pathway toward intelligent aero-engine control.

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