Carrier Aircraft Landing Control Technology Based on Deep Reinforcement Learning

In this paper, a pitching control method based on Deep Deterministic Policy Gradient (DDPG) algorithm for carrier aircraft landing and descending stage is studied. DDPG controller takes pitch angle rate error, pitch angle error and altitude error as input, and output as elevator deflection, realizing the rapid pitch angle response of carrier-aircraft under different landing states. Compared with traditional PID controller, network training of Actor-Critic for DDPG attitude controller greatly improves the calculation efficiency of control quantity, and reduces the difficulty of parameter optimization. The simulation experiment in this paper was based on the F/A-18 aircraft aerodynamics model constructed in Matlab/Simulink, and the intensive learning and training environment built on PyCharm platform was used to realize the data interaction between the two platforms through UDP communication. The simulation results show that the attitude controller based on reinforcement learning designed in this paper has the characteristics of fast response speed and small dynamic error, and meets the control accuracy requirements in the experiment.