A kind of pitch channel dynamic model of hypersonic aircraft considered with both the model of engine and the model of elastic shape is studied. A special typical flying point is chosen that the state of elastic shape is assumed to be constant and the engine is designed with a PID law to make the speed of aircraft close to a constant. Then a kind of hybrid controller based on FLNN neural network and PID control is designed to make the attack angle can converged to desired value. And the adoption of Taylor type FLNN neural network can make use of the form of air dynamic coefficient which is a function like Taylor series, so it has a good adaptive ability to compensate the uncertainties and unconsidered factors of hypersonic model. And the use of PID control law can make use of the advantage of traditional classic control theory and what is the most important of all is that the PID control can provide enough damp ratio to make the system stable enough. So the hybrid control strategy can integrate both advantage of neural network and PID control methods which is also testified by the detailed numerical simulation in the last part of this paper.

中文翻译：

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基于PID和FLNN神经网络的高速车辆攻角跟踪研究。

研究了一种同时考虑发动机模型和弹性形状模型的高超音速飞机俯仰通道动力学模型。选择一个特殊的典型飞行点，即假定弹性形状的状态是恒定的，并且发动机采用PID律进行设计，以使飞机的速度接近恒定。然后设计了一种基于FLNN神经网络和PID控制的混合控制器，使攻角可以收敛到期望值。泰勒型FLNN神经网络的采用可以利用泰勒级数等函数的空气动力系数形式，具有良好的自适应能力，可以补偿高超声速模型的不确定性和未考虑因素。PID控制律的使用可以利用传统经典控制理论的优势，最重要的是PID控制可以提供足够的阻尼比以使系统足够稳定。因此，混合控制策略既可以融合神经网络的优势，又可以结合PID控制方法，这在本文的最后一部分进行了详细的数值仿真，也证明了这一点。