Second Order Sliding Mode Control of Flying Wing Aircraft Based on Feedforward Neural Networks (I)
Yuecheng Song, Zhenbao Liu, Junwei Han, Jinbiao Yuan, Wen Zhao, Qingqing Dang
Abstract
The flying-wing aircraft control problem is a major concern. In this paper, a new control strategy is introduced. First, a Feedforward neural network (FNN) modeling is introduced. Then, a second-order sliding mode control is applied, with the parameters generated from Deep Deterministic Policy Gradient (DDPG) reinforcement learning. To study the disturbance rejection performance, wind disturbance is applied to the aircraft using a deep neural network as an disturbance observer for different types of winds. Finally, All three simulations: Simulink, Software In The Loop, and Hardware In the Loop are applied to show the effectiveness of the proposed strategy. The simulation results show that the proposed method demonstrates good robustness in various conditions.