A typical adaptive neural control methodology is used for the rigid body model of the hypersonic vehicle. The rigid body model is divided into the altitude subsystem and the velocity subsystem. The proportional integral differential (PID) controller is introduced to control the velocity track. The backstepping design is applied for constructing the controllers for the altitude subsystem. To avoid the explosion of differentiation from backstepping, the higher-order filter dynamic is used for replacing the virtual controller in the backstepping design steps. In the design procedure, the radial basis function (RBF) neural network is investigated to approximate the unknown nonlinear functions in the system dynamic of the hypersonic vehicle. The simulations show the effectiveness of the design method.

中文翻译：

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基于高阶滤波器的超音速飞行器典型自适应神经控制

典型的自适应神经控制方法用于高超音速飞行器的刚体模型。刚体模型分为高度子系统和速度子系统。引入比例积分微分（PID）控制器来控制速度轨迹。反推设计用于构建海拔子系统的控制器。为了避免反演带来的差异性爆炸，在反演设计步骤中使用了高阶滤波器动态替换虚拟控制器。在设计过程中，研究了径向基函数（RBF）神经网络，以近似高超声速飞行器系统动力学中未知的非线性函数。仿真结果表明了该设计方法的有效性。