| description abstract | Traditional aerodynamic modeling methods have some difficulties in multiparameter unsteady state-space modeling and control law design, which raise significant challenges in flight control. The rapid development of machine learning provides a new idea for unsteady aerodynamic modeling and control law design, which has significant value for theoretical research and engineering application. In this paper, an unsteady aerodynamic model environment was established based on deep neural network (DNN), and a dynamic computational fluid dynamics (CFD) virtual environment was taken as an approximation of the real environment. A deep reinforcement learning (DRL) longitudinal-axis flight control method was studied on the basis of both environments. The deep deterministic policy gradient (DDPG) algorithm was used to implement flight control, and the effects of different constraints in the reward function on the results of DRL were studied at the same time. The results show that the DDPG agent effectively achieves longitudinal-axis flight control in the model environment. The DDPG agent trained in the model environment was also used for longitudinal-axis flight control in the dynamic CFD virtual environment, and this method provides a reference for the coupling of dynamic CFD and DRL. The control results from the model environment and the dynamic CFD virtual environment are compared, and results show that the DDPG agent has good robustness in both environments. The results of this study suggest that machine learning and deep reinforcement learning can effectively solve complicated multiconstraint, uncertain, and sophisticated control tasks. In this paper, a DNN-based unsteady aerodynamic modeling method was established based on dynamic computational fluid dynamics calculation and machine learning, and the results show that the DNN-based unsteady aerodynamic model has advantages in dealing with highly nonlinear data under unsteady conditions. In addition, the deep deterministic policy gradient algorithm can be well applied in longitudinal-axis flight control, and the agent can be trained in DNN-based unsteady aerodynamic model environment. The agent was also used for longitudinal-axis flight control in the dynamic computational fluid dynamics virtual environment, and the results show that the agent has good robustness in both environments. This provides the possibility for the agent to achieve longitudinal-axis flight control in a real environment. | |
