Cascaded Learning Generation Framework for Quadrotor UAV Maneuvering Simulation Models

The quadrotor unmanned aerial vehicle (UAV) is widely used due to its low maintenance cost, high maneuverability and strong hovering capability. Modeling the quadrotor UAV maneuver and simulating its performance can effectively support airborne intelligent algorithms training such as mission planning and scheduling. Traditional quadrotor UAV maneuver modeling method construct high-order mathematical model based on physics analysis, which require significant expertise and difficult to generalize. In this paper, we analyze the quadrotor UAV maneuvering process and propose a cascaded quadrotor UAV maneuvering model generating framework based on deep neural network. Using long short-term memory (LSTM) network to model each part of the quadrotor UAV maneuvering process individually, and flexibly combine network of each part to obtain varying granularity models. A variable-dimensional particle swarm optimization (PSO) algorithm based on detour foraging strategy is proposed to simultaneously determine the LSTM network's hidden layers and neurons of each hidden layer. We validate the effectiveness of the maneuvering model generation framework and the improved PSO algorithm through comparative experiments.