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.
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