Direct lift control technology of carrier aircraft landing based on reinforcement learning

The direct lift control method of automatic landing based on Proximal Policy Optimization (PPO) algorithm was proposed to solve the problem that it is easy to touch ship due to disturbance of deck movement and carrier air wake during automatic landing of carrier aircraft. The PPO controller takes six state variables of pitch angle, height, flight path angle, pitch angle rate, height error and flight path angle rate as input and output as flap deflection angle, realizing the rapid response of carrier aircraft in different landing states of flight path angle. Compared with traditional PID controller, the Actor-Critic network in PPO controller greatly improves the calculation efficiency of control quantity, and also reduces the difficulty of parameter optimization. The simulation experiment in this paper is based on the dynamics/kinematics model of F/A-18 aircraft constructed in Matlab/Simulink. The intensive learning and training environment built on PyCharm platform is used to realize the data interaction between the two platforms through user datagram protocol (UDP) communication. The simulation results show that the proposed method has the characteristics of fast response speed and small dynamic error, and can stabilize the landing height error within ±0.2 m, with high control accuracy.