基于深度强化学习的无人机智能飞行控制试验OA

Deep Reinforcement Learning(DRL)provides a new technological paradigm for the intelligent flight control of unmanned aerial vehicles.However,the confidence in the"black box"Artificial Neural Network(ANN)intelligent model is the main obstacle to practical application.To validate the neural network-based intelligent flight control model designed with DRL through flight test,a longitudinal end-to-end intelligent flight control model that maps the flight state to the elevator/thrust commands is developed for a fixed-wing scaled model aircraft,based on the multi-dimensional continuous state input and action output DRL Proximal Policy Optimization(PPO)algo-rithm.The robustness of ANN control model is validated through the simulation,and its engineering implementa-tion for the sim-to-real transfer is further carried out.A flexible onboard ANN controller is developed and the mod-el flight demonstration is launched.The test results preliminarily verify the applicability and generalization perfor-mance of the ANN controller.