Research on Optimal Control Strategy and Method for Scramjet Fuel Pulse Injection

This study investigates optimal control strategies for fuel pulse injection in scramjets under varying inflow conditions and thrust demands. A one-dimensional mathematical model was developed through CFD simulation data to capture the nonlinear relationships between injection parameters and performance metrics, enabling efficient control simulations. A pulse injection model was established, and an active disturbance rejection control (ADRC) algorithm was proposed to effectively manage injection parameters, demonstrating strong disturbance rejection capabilities essential for variable conditions. To address real-time tuning challenges, a real-time adjustment method (RL-ADRC) based on the twin delayed deep deterministic policy gradient (TD3) algorithm was introduced, allowing dynamic ADRC parameter adjustments based on environmental and operational states, achieving superior performance over traditional methods. Furthermore, a residual neural-network-based optimal control strategy was proposed to determine pulse injection parameters for varying thrust demands, ensuring desired thrust output, preventing inlet unstart, and maintaining high combustion efficiency. Comprehensive simulations validated the proposed strategies, confirming the robust performance of the pulse injection model, RL-ADRC, and optimal control strategy. These methods demonstrate strong potential to enhance the reliability and efficiency of scramjet operations under complex and dynamic conditions.