In order to solve the multidimensional restricted optimization problem of aeroengine acceleration process and improve the acceleration performance of full envelope, a design method of aeroengine full envelope acceleration controller based on deep reinforcement learning is proposed. Firstly, the thermo-aerodynamic principle of the engine acceleration process is analyzed, and the mathematical analytical model of the multidimensional restricted optimization problem of the turbofan engine acceleration process is established. On this basis, the design method of acceleration controller based on twin delayed deep deterministic policy gradient is studied. The proportional integral controller is designed to pursue the asymptotic stability at the acceleration destination and stable control is achieved through switching. Additionally, the flight envelope is divided into regions by clustering to reduce the span of data changes, so as to enhance the network convergence ability of deep reinforcement learning. Finally, based on the similarity conversion method of equal engine inlet total temperature, the acceleration controller of full flight envelope is realized. The digital simulation results demonstrate that compared with the traditional proportional integral differential controller, the acceleration controller based on twin delayed deep deterministic policy gradient shortens the acceleration time up to 48.33%, and the control effect in a large flight envelope ($H=0\sim$10 km, $Ma=0\sim$1.6) has been verified.

为解决航空发动机加速过程的多维约束优化问题，提高全包络加速性能，提出一种基于深度强化学习的航空发动机全包络加速控制器设计方法。首先分析了发动机加速过程的热气动原理，建立了涡扇发动机加速过程多维约束优化问题的数学分析模型。在此基础上，研究了基于双延迟深度确定性策略梯度的加速度控制器设计方法。比例积分控制器的设计目标是追求加速终点的渐近稳定，通过切换实现稳定控制。此外，通过聚类对飞行包线进行区域划分，减少数据变化的跨度，从而增强深度强化学习的网络收敛能力。最后，基于等发动机进气道总温的相似变换法，实现了全飞行包线加速度控制器。数字仿真结果表明，与传统的比例积分微分控制器相比，基于双延迟深度确定性策略梯度的加速控制器加速时间缩短达48.33%，大飞行包线下的控制效果（基于等发动机进气总温的相似变换法，实现了全飞行包线加速度控制器。数字仿真结果表明，与传统的比例积分微分控制器相比，基于双延迟深度确定性策略梯度的加速控制器加速时间缩短达48.33%，大飞行包线下的控制效果（基于等发动机进气总温的相似变换法，实现了全飞行包线加速度控制器。数字仿真结果表明，与传统的比例积分微分控制器相比，基于双延迟深度确定性策略梯度的加速控制器加速时间缩短达48.33%，大飞行包线下的控制效果（$H=0～$10公里，$米A=0～$1.6) 已经过验证。