Second-Order Sliding Mode Control of Flying-Wing Aircraft Based on Feedforward Neural Networks

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. Note to Practitioners—This paper is motivated by the traditional linearized flying-wing aircraft controller with the effectiveness of the FNN and reinforcement learning on UAV applications. The controller can be designed for various situations without changing the parameters by modeling the aircraft through the FNNs. The theoretical framework proposed in this paper combines the multiple-input multiple-output (MIMO) sliding mode strategy with the reinforcement learning with higher accuracy modeling. This method reduces the settling time, overshoot and steady error. More realistic effects should be considered for deployment into real aircraft. The parameters of the neural networks should also be adjusted in real applications.The FNN networks and DDPG have low computational footprints and are readily deployable on embedded systems like Pixhawk. The DNN observer may require model compression for the smallest processors. While the current framework requires per-aircraft training to achieve optimal performance, this process is conducted offline. The resulting controller gains are then fixed for reliable real-time operation, providing a clear pathway for implementation on specific UAV platforms.