跨域变构飞行器自学习模型预测姿态控制方法

The large-flight-envelope morphing flight vehicle can autonomously adjust its aerodynamic shape according to the environment to achieve optimal aerodynamic performance for large-flight-envelope mission. To address the significant changes in the dynamic model caused by morphing and the nonlinear uncertainties in aerodynamic disturbances during the glide phase， a self-learning model predictive attitude control method is proposed. This method involves learning of control parameters based on the system's own model and data under random disturbances and configurations to enhance the controller's robustness. Based on the parametric model predictive control problem， this method treats model deviations and configurations as random variables to reduce the cost function of the stochastic optimal control problem through parameter learning， and obtains the optimal model predictive control parameters for the morphing flight vehicle. Simulation results for different morphing missions show that this control method can maintain good control quality under a 30% deviation in aerodynamic parameters， and improve attitude tracking response speed compared to untrained parameters.