本文针对大尺度挠性空间结构姿态镇定控制问题，充分考虑振动模型参数未知、刚挠耦合关系未知等因素，提出了一种基于物理信息循环神经网络的（Physics-informed Recurrent Neural Network, PI-RNN）的新型模型预测姿态控制方法。首先，通过将大尺度挠性空间结构的先验物理振动方程嵌入循环神经网络（Recurrent Neural Networks, RNN），构建了挠性附件振动的预测模型，实现了对大尺度空间结构振动模态的精准在线预测。而后，将所提出的PI-RNN振动预测模型与非线性模型预测控制（Nonlinear Model Predictive Control, NMPC）相结合，克服了传统NMPC方法高度依赖精确系统动力学模型的问题，并基于Lyapunov理论分析了闭环控制系统的稳定性与可行性。与传统航天器姿态控制方法相比，所提方法在实现既定性能指标最优性的同时，具备通过PI-RNN实时数据学习克服振动参数漂移问题的潜在能力。最后，数值仿真实验证明了所提方法的有效性和优越性。

A novel model predictive attitude control method based on a Physics-informed Recurrent Neural Network (PI-RNN) is pro-posed for the attitude stabilization control problem of large-scale flexible space structures, which fully considers factors such as unknown vibration model parameters and unknown rigid-flexible coupling relationships. Firstly, a PI-RNN predictive mod-el for vibrations of flexible appendages is developed by embedding the prior physical vibration dynamics of large-scale flexi-ble space structures into a Recurrent Neural Network (RNN), which facilitates accurate online prediction of the vibration modes of large-scale space structures. Then, the proposed PI-RNN predictive model is further integrated into a Nonlinear Model Predictive Control (NMPC) framework to overcome the problem that traditional NMPC methods are highly dependent on model accuracy. Moreover, the stability and feasibility of the closed-loop control system are analyzed based on Lyapunov theory. Compared with traditional spacecraft attitude control methods, the proposed approach not only achieves the optimality of predefined performance metrics but also exhibits the potential to overcome vibration parameter drift through real-time data learning via the PI-RNN. Finally, numerical simulations demonstrate the effectiveness and superiority of the proposed method.