Stochastic Model Predictive Control for Parafoil System via Markov-Based Multi-Scenario Optimization

As an essential technology for precision airdrop missions, parafoil systems have gained widespread adoption in military and civilian applications due to their superior glide performance and maneuverability compared to conventional parachutes. Addressing the trajectory-tracking control challenges of the parafoil system under significant wind disturbances, characterized by wind uncertainty and system underactuation, this paper proposes a stochastic model predictive control (SMPC) framework based on Markov-based multi-scenario optimization. Traditional deterministic model predictive control (MPC) methods often exhibit excessive conservatism due to reliance on worst-case assumptions and fail to capture the time-varying nature of real-world wind fields. To address these limitations, a high-fidelity dynamic model is developed to accurately characterize aerodynamic coupling effects, overcoming the oversimplifications of conventional three-degree-of-freedom point-mass models. Leveraging Markov state transitions, multiple wind-disturbance scenarios are dynamically generated, effectively overcoming the limitations of independent and identically distributed hypotheses in modeling realistic wind variations. A probabilistic constraint-reconstruction strategy combined with a rolling time-domain covariance update mechanism mitigates uncertainties and enables cooperative optimization of inner-loop attitude stabilization and outer-loop trajectory tracking. The simulation results demonstrate that the SMPC framework achieves superior comprehensive performance compared to deterministic MPC, evidenced by significant reductions in maximum position error, average position error, and control effort variation rate, along with a 94% tracking success rate. By balancing robustness, tracking precision, and computational efficiency, the method provides a theoretical foundation and a promising simulation-validated solution for airdrop missions.