Bingnan Xing

To address the challenges of discrete control and real-time decision-making for impulse-controlled vehicles, this study proposes a landing-point prediction and control algorithm based on a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network. The algorithm employs the Bi-LSTM network to accurately predict the vehicle landing point in real time, and subsequently computes the vehicle–target deviation to determine the optimal impulse-ignition parameters. Simulation results demonstrate that the Bi-LSTM prediction model significantly outperforms traditional BP, LSTM, and GRU networks, achieving a final landing-point prediction error with a mean per-axis RMSE of 1.96 m (three-dimensional error norm of 3.87 m). Based on these prediction outcomes, two control strategies—“nearest-point ignition” and “ignition delay”—are compared. Monte Carlo experiments indicate that both strategies effectively enhance landing accuracy, with the nearest-point ignition strategy showing superior performance, reducing the circular error probable (CEP) from the uncontrolled 72.60 m and 124.82 m (at launch angles of 23° and 51°, respectively) to 7.20 m and 10.30 m. This study provides a high-precision, terminal-phase control solution for impulse-controlled vehicles, with implications for the low-cost onboard implementation of discrete impulse control.

To improve the tactical and technical performance of pulse correction projectiles while maintaining stability in uncertain conditions and considering practical engineering constraints, this study performs a multi-objective robust optimization design of the aerodynamic shape and external ballistics of a projectile. The study utilizes an aerodynamic force engineering algorithm and numerical trajectory calculations to obtain the projectile’s performance responses within the Latin hypercube design space. To enhance optimization efficiency, a stochastic Kriging surrogate model is established to capture the inherent uncertainty of limited input data. Ultimately, a Pareto optimal solution for the projectile is obtained using a non-dominated sorting multi-objective sparrow search algorithm. The results of this study demonstrate that the consideration of design uncertainty in the robust optimization of pulse correction projectiles leads to significant enhancements in both lateral correction ability and range while satisfying flight stability requirements. Moreover, when compared to deterministic optimization, the performance variability of the design is markedly improved. This research methodology provides valuable insights for optimizing the performance of pulse correction projectiles.