Missile Attitude Control Based on Deep Reinforcement Learning

Deep reinforcement learning (DRL) has been one of the research hotspots in the areas of control. In this paper, we focus on the study of missile attitude control system using DRL. An novel PID controller based on deep deterministic policy gradient(DDPG) algorithm is presented, which could applied to the self-tuning of parameters. The framework of the adaptive DDPG-PID controller is given. The controller takes flight information as input and takes rudder angle as output. A reward function related to the system error is designed, which can be used to train the DDPG algorithm effectively. Simulation results show that the adaptive DDPG-PID controller has a faster convergence velocity, reduces the overshoot and oscillation, achieves higher accuracy tracking control to target.