Aiming at the cooperative guidance problem of multi-hypersonic glide vehicles, a cooperative guidance method based on a parametric design and an analytical solution of time-to-go is proposed. First, the hypersonic reentry trajectory optimization problem was transformed into a parameter optimization problem. The parameters were optimized to determine the angle of attack profile and the time to enter the altitude velocity reentry corridor. Then, using the quasi-equilibrium glide condition, the estimation form of the remaining flight time was analytically derived to satisfy accurately the cooperative time constraint. Using the remaining time-to-go and range-to-go, combined with the heading angle deviation corridor, the bank angle command was further calculated. Finally, the swarm intelligence optimization algorithm was used to optimize the design parameters to obtain the cooperative guidance trajectory satisfying the time constraint. Simulations showed that the analytical time reentry cooperative guidance algorithm proposed in this paper can accurately meet the time constraints and cooperative flight accuracy. Monte Carlo simulation experiments verified that the proposed algorithm demonstrates a robust performance.

This paper presents a systematic design of a predefined-time sliding mode controller with integrated prescribed performance strategies, tailored to the longitudinal dynamics of hypersonic vehicles (HSVs) operating under strict real-time and high-precision requirements. A prescribed error transformation function is introduced to simultaneously constrain both transient and steady-state behaviors. This transformation converts the original constrained tracking problem into an equivalent unconstrained stabilization problem, thereby simplifying the controller synthesis. Based on the decomposed control-oriented state-space equations, separate sliding mode controllers are designed for the velocity and attitude subsystems. The proposed strategy guarantees that the tracking errors converge to zero within a user-predefined time, while strictly satisfying the prescribed performance bounds at every stage of the closed-loop response. The efficacy of the method is validated through numerical simulations.

Trajectory optimization of hypersonic vehicles face challenges from complex aerodynamic environments and multiple constraints, where traditional offline optimization methods struggle to meet real-time requirements. This study proposes a novel online trajectory optimization framework for hypersonic vehicles that integrates a multi-strategy improved whale optimization algorithm (MWOA) with an attention-mechanism Long Short-Term Memory (AM-LSTM) network. First, an offline trajectory dataset under aerodynamic uncertainties is generated using sequential second-order cone programming (SOCP). Subsequently, a multi-head attention mechanism is incorporated into the LSTM network to effectively capture sequential dependencies within the trajectory data. To automate the hyperparameter tuning of the AM-LSTM architecture, a multi-strategy improved whale optimization algorithm is developed, which incorporates circle chaotic mapping for population initialization, a nonlinear convergence factor to balance global and local search, and a dynamic golden-sine mutation strategy to enhance optimization robustness. The trained MWOA-AM-LSTM hybrid model is then employed for real-time trajectory generation. Numerical simulation results demonstrate that the proposed framework achieves superior terminal accuracy under aerodynamic perturbations, validating its effectiveness and robustness for hypersonic vehicle reentry trajectory optimization.