Reliability-driven adaptive multi-level pre-optimization control method for reusable launch vehicles under strong stochastic wind disturbances

Abstract

Strong stochastic wind disturbances pose a major threat to the landing reliability of reusable launch vehicles, potentially leading to attitude instability, trajectory deviation, and even structural damage. Traditional control methods, which heavily rely on precise dynamic models and online state observation or prediction, often struggle to ensure reliability under such conditions. To address this challenge, this work proposes a reliability-driven landing pre-optimization method that incorporates regional stochastic wind characteristics, thereby eliminating the need for online observation or prediction. The landing dynamics and stochastic wind field models are established to quantify the destructive impact of winds on landing reliability. An adaptive multi-level control strategy is then introduced, which hierarchically deploys simplified control laws based on real-time altitude and velocity. This design effectively compensates for time-varying wind disturbances without depending on online observers or pre-planned trajectories. Furthermore, a reliability-driven offline optimization framework is developed to tune the control parameters and landing initiation conditions. These key parameters are optimized offline through large-scale Monte Carlo simulations across diverse wind scenarios, thus avoiding the computational burden of online optimization. Finally, the optimal parameters and conditions are pre-optimized to adapt to regional stochastic winds. Results demonstrate that the proposed method achieves a landing reliability of >99.5% and reduces the maximum landing deviation by 99.52%. In comparative studies, the offline pre-optimization method shows superior performance to typical online Model Predictive Control, improving reliability by 14.1%. The proposed strategy offers a robust and practical solution for achieving high-reliability landings under strong stochastic winds.