Identification Method of SUAV in Diving Phase Based on Flight Tests

Abstract

A mathematical model of the dive phase is an important research content for improving the accuracy of terminal control in the small unmanned aerial vehicle. The acquisition of the diving model poses new challenges, such as the small installation space, ultra-low flying height of small suicide drones, short flight time, strong coupling, less observable measurement, and elastic deformation of the wings during the drone dive phase. Based on the autoregressive moving average method, a multi-input multioutput noise term topology mathematical model is proposed in this paper. Through an improved least squares identification method, the diving model in the flight test is analyzed and verified. The identification results of the diving model obtained by the proposed method are compared with the least squares method dive model. The results indicate that the mathematical model and identification method proposed in this paper can effectively obtain the parameters of the drone dive model.

Optimization of UAV Airfoil Based on Improved Particle Swarm Optimization Algorithm

“…With the continuous expansion of the military use of UAVs, the tasks undertaken by UAVs have higher and higher requirements for the basic performance of UAVs [6]. Selection and optimization of airfoils are critical tasks in UAV design [7,8].…”