首先给出理想情况下固定翼无人机的姿态动力学模型, 然后在此基础上根据动态逆方法设计出无人机姿态控制器,但基于此控制器并不适合实际情况下的无人机模型, 原因在于无人机实际模型与理想模型之间存在一定的偏移, 即模型不确定, 而且在实际情况中还存在一定的干扰, 因此,在原有无人机姿态控制器的基础上结合模糊神经网络, 来补偿无人机运行过程中存在的模型不确定及干扰。

Firstly, the attitude dynamic model of the fixed-wing UAV under ideal conditions is given, and then, the UAV attitude controller is designed based on the dynamic inverse method.But the controller above is not suitable for the UAV model under actual conditions, because there is a certain offset between the actual model and the ideal model, which is model uncertainty.Moreover, there are also some external disturbances in the actual situation.Therefore, the fuzzy neural network is combined with the original UAV attitude controller to compensate for the model uncertainty and external disturbances existed in the operation of the UAV.