PI-RNN增强的大尺度挠性空间结构模型预测姿态稳定控制

doi:10.7527/S1000-6893.2025.32734

Abstract

Abstract:

A novel model predictive attitude control method based on a Physics-informed Recurrent Neural Network （PI-RNN） is proposed 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. First， a PI-RNN predictive model for vibrations of flexible appendages is developed by embedding the prior physical vibration dynamics of large-scale flexible 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.

Key words: large-scale flexible space structure, attitude stabilization control, physics-informed recurrent neural network, vibration mode prediction, nonlinear model predictive control

CLC Number:

V249.1

Guoliang LYU, Zhiqiang BIAN, Yanning GUO, Pengyu WANG. Physics-informed recurrent neural network enhanced model predictive control for large-scale flexible space structure attitude stabilization[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(15): 232734.

Figures/Tables 13

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Table 1

Simulation parameters

参数	取值
挠性空间结构转动惯量 $J$ /（ $k g ⋅ m 2$ ）	$d i a g [2 046,1 924,2 678]$
初始姿态四元数 $q (0)$	$[0.98,0.122, - 0.097,0.075] T$
初始角速度 $ω (0)$ /（ $r a d ⋅ s - 1$ ）	$[0.01, - 0.008,0.005] T$
振动模态初始值 $η (0)$	$[0.18, - 0.60,0.44,0.77, 0.7,0.50,0.35] T × 10 - 2$
控制力矩约束/（ $N ⋅ m$ ）	$- 20,20$
状态权重矩阵 $Q$	$10 I 4 0 0 25 I 3$
控制权重矩阵 $R$	$10 - 5 I 3$
终端代价权重矩阵 $P$	$5 Q$
采样时间 $d t$ / $s$	$0.1$
预测步长 $N$	$5$
仿真时间 $T$ / $s$	$100$

Table 1

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Physics-informed recurrent neural network enhanced model predictive control for large-scale flexible space structure attitude stabilization

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