This study proposes an effective adaptive dynamic surface control (DSC) method based on the radial basis function neural networks and the auxiliary system for hypersonic flight vehicle (HFV) systems in the presence of system uncertainties, external disturbances, and state variable and control input constraints. Firstly, to enhance the robustness of the system, the neural network is combined with the robust term to deal with the uncertainties and external disturbances of the system. Secondly, to prevent the deterioration of the dynamic performance of the system due to the over‐adaptation of the neural networks and the robust terms caused by the state and control input constraints, the auxiliary system is added at each step in the DSC design to adjust the dynamic process of the reference signal and virtual control. Furthermore, the variable structure control is used to solve the problem of dead zone in the control input. Using the Lyapunov analysis method, all signals of the closed‐loop system are semi‐globally uniformly ultimate bounded. The simulation results illustrate the effectiveness of the proposed control scheme for the HFVs.

中文翻译：

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具有输入非线性的高超音速飞行器的神经网络自适应动态表面控制

这项研究提出了一种基于径向基函数神经网络和高超声速飞行器（HFV）系统辅助系统的有效自适应动态表面控制（DSC）方法，该方法存在系统不确定性，外部干扰以及状态变量和控制输入约束的情况。首先，为了提高系统的鲁棒性，将神经网络与鲁棒性项相结合来处理系统的不确定性和外部干扰。其次，为防止由于神经网络的过度适应以及状态和控制输入约束导致的健壮项而导致系统动态性能的下降，在DSC设计的每个步骤都添加了辅助系统以进行调整参考信号的动态过程和虚拟控制。此外，可变结构控制用于解决控制输入中的死区问题。使用Lyapunov分析方法，闭环系统的所有信号都是半全局一致的极限极限。仿真结果说明了所提出的HFV控制方案的有效性。