基于神经网络性能模型的无人机起飞决断技术研究

UAV Takeoff Decision Based on Neural Network Model of Takeoff Capability

为增加无人机单发停车起飞的安全性,提出了一种基于神经网络性能模型的无人机起飞决断方案。将满足安全性能指标的继续起飞最小速度和中止起飞最大速度作为单发决断的关键性能参数,设计了在多种飞行条件下迭代计算两种性能指标的闭环仿真算法。采用双层神经网络来模拟多种飞行情况下的性能数据库以实现性能库的压缩存储和高精度调用。给出了基于神经网络性能模型在线计算值的无人机起飞决断策略。仿真计算表明,所提出的起飞决断方案工程实现性强,能够增强无人机单发起飞的安全鲁棒性。

To enhance the safety in case of engine flameout failure, a new type of UAV takeoff decision based on neural network capacity model was proposed. Two capacity parameters of takeoff safety in case of engine flameout failure were defined, one is the maximum velocity for a safe takeoff and the other is the minimum velocity for a safe shut down. A calculation method based on iterative simulations for those parameters under multiple flight conditions was introduced. Double layer neural networks were used to model the relationship between flight conditions and the capacity parameters, to realize the compressive storage and high precision adopted of the parameters. A takeoff decision based on online capacity calculation values from neural network capacity model of the UAV was derived. Simulation results show the strong practicality and benefit for enhancing UAV safety robustness of the proposed take off decision strategy.