An Intelligent Attitude Control Method for UAV Based on DDPG Algorithm

Abstract

The unmanned aerial vehicle (UAV) requires the control system with fast response speed, high accuracy and strong robustness. Due to the strong coupling and nonlinearity of UAV system, it is difficult to build the accurate system model, which makes it difficult for traditional linear control methods to achieve accurate and stable control effects. This paper proposes an intelligent attitude control method for high-speed UAV. Firstly, the nonlinear attitude motion model of UAV is established and a new intelligent attitude controller structure is built based on the Deep Deterministic Policy Gradient (DDPG) algorithm. Secondly, the DDPG attitude controller is trained by the simulation training system. The control simulation results show that the proposed intelligent control system and controller can meet the requirements of attitude adjustment and attitude stabilization, the control accuracy error is less than 0.02 degree. Finally, considering fixed mutation interference, Gaussian noise and 50% deviation of aerodynamic parameters disturbance, the control accuracy error is still less than 0.1 degree during the interference test. The stability analysis shows that the intelligent controller has strong robustness and generalization ability.