This study develops a novel neural-approximation-based prescribed performance controller for flexible hypersonic flight vehicles (HFVs). Firstly, a new prescribed performance mechanism is exploited, which develops new performance functions guaranteeing velocity and altitude tracking errors with small overshoots. Compared with the existing prescribed performance mechanism, it has better preselected transient and steady-state performance. Then, the nonaffine model of HFV is decomposed into a velocity subsystem and an altitude subsystem. A prescribed performance-based proportional-integral controller is designed in the velocity subsystem. In the altitude subsystem, the model is expressed as a nonaffine pure feedback form, and control inputs are derived from neural approximations. In order to reduce the amount of computation, only one neural network approximator is used to approximate the subsystem uncertainties, and an advanced regulation algorithm is applied to the devise adaptive law for neural estimation. At the same time, the complex design process of back-stepping can be avoided. Finally, numerical simulation results are presented to verify the efficiency of the design.

中文翻译：

---

具有非仿射动力学和神经近似的柔性超音速飞行器的新型规定性能控制方案

这项研究为柔性高超音速飞行器（HFV）开发了一种新型的基于神经近似的规定性能控制器。首先，开发了一种新的规定性能机制，该机制开发了新的性能函数，可确保速度和高度跟踪误差且超调量较小。与现有的规定性能机制相比，它具有更好的预选瞬态和稳态性能。然后，HFV的非仿射模型被分解为速度子系统和高度子系统。在速度子系统中设计了一个基于性能的预定比例积分控制器。在海拔子系统中，模型表示为非仿射纯反馈形式，而控制输入则来自神经近似。为了减少计算量，仅使用一个神经网络逼近器来逼近子系统不确定性，并且将一种先进的调节算法应用于设计用于神经估算的自适应定律。同时，可以避免复杂的反步设计过程。最后，通过数值仿真结果验证了设计的有效性。