This paper provides a solution for the trajectory tracking control of a hypersonic flight vehicle (HFV), which is encountered performance constraints, actuator faults, external disturbances, and system uncertainties. For the altitude and velocity control subsystems, the backstepping-based dynamic surface control (DSC) strategy is constructed to guarantee the predefined constraint of tracking errors. The introduction of first-order low-pass filters effectively remedies the problem of “complexity explosion” existing in high-order backstepping design. Simultaneously, radial basis function neural networks (RBFNNs) are adopted for approximating the unavailable dynamics, in which the minimum learning parameter (MLP) algorithm brilliantly alleviates the excessive occupation of the computational resource. Specially, in consideration of the unknown actuator failures, the adaptive signals are designed to enhance the reliability of the closed-loop system. Finally, according to rigorous theoretical analysis and simulation experiment, the stability of the proposed controller is verified, and its superiority is exhibited intuitively.

中文翻译：

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具有执行器故障的超音速车辆的规定性能跟踪控制

本文为高超声速飞行器（HFV）的轨迹跟踪控制提供了一种解决方案，该解决方案遇到性能限制，执行器故障，外部干扰和系统不确定性的问题。对于高度和速度控制子系统，构建了基于反步的动态表面控制（DSC）策略，以确保跟踪误差的预定义约束。一阶低通滤波器的引入有效地解决了高阶反推设计中存在的“复杂性爆炸”问题。同时，采用径向基函数神经网络（RBFNN）对不可用的动力学进行近似，其中最小学习参数（MLP）算法很好地缓解了计算资源的过度占用。特别，考虑到未知的执行器故障，自适应信号旨在提高闭环系统的可靠性。最后，通过严格的理论分析和仿真实验，验证了所提出控制器的稳定性，并直观地展示了其优越性。