基于李雅普诺夫监督的物理信息神经网络校正层的欠驱动航天器MPC控制
MPC for underactuated spacecraft control with a Lyapunov supervised physics-informed neural network correction layer
AI总结：
针对欠驱动航天器姿态控制，提出一种分层架构，结合非线性模型预测控制、物理信息神经网络和李雅普诺夫监督机制，在不确定性下降低稳态误差并保持鲁棒性。
摘要：
欠驱动航天器面临可控性限制和对环境干扰的高度敏感性，使得姿态机动和稳定复杂化。由于沿欠驱动轴缺乏控制能力，传统控制器无法直接稳定所有姿态分量，因此需要参考规划策略。此外，MPC方法对惯性不确定性和未建模动态耦合仍然敏感，导致在失配下跟踪性能下降。为解决这些问题，我们考虑一种集成三层的分层架构：(i) 非线性模型预测控制器（NMPC），用于约束和欠驱动感知的机动规划以及在执行器限制下的标称闭环稳定性；(ii) 物理信息神经网络（PINN），在仿真数据上离线训练以估计残余干扰力矩，其损失项强制执行与刚体旋转动力学的一致性；(iii) 基于李雅普诺夫的监督安全机制，在线评估学习到的校正并限制或抑制其影响，以保持基线控制器的稳定性特性。该架构在模拟反作用轮动力学、执行器饱和及环境干扰的高保真仿真环境中进行评估。蒙特卡洛研究表明，与独立NMPC相比，稳态姿态误差有统计显著的降低，同时在不确定性下保持鲁棒行为。监督层确保当基于学习的增强不可靠时，能够优雅地退化到纯模型控制。
英文摘要：
Underactuated spacecraft faces controllability limitations and heightened sensitivity to environmental disturbances, complicating attitude maneuvering and stabilization. Due to the lack of control authority along the underactuated axis, conventional controllers cannot directly stabilize all attitude components and therefore require reference planning strategies. Furthermore, MPC approaches remain sensitive to inertia uncertainty and unmodeled dynamic couplings, resulting in degraded tracking performance under mismatch. To address these issues, we consider a hierarchical architecture integrating three layers: (i) a nonlinear model predictive controller (NMPC) for constraint and underactuation-aware maneuver planning and nominal closed-loop stability under actuator limits; (ii) a physics-informed neural network (PINN) trained offline on simulation data to estimate residual disturbance torques, with loss terms that enforce consistency with rigid-body rotational dynamics; (iii) a Lyapunov-based supervisory safety mechanism that evaluates the learned correction online and bounds or suppresses its influence to preserve the stability properties of the baseline controller. The architecture is evaluated in a high-fidelity simulation environment modelling reaction wheel dynamics, actuator saturation, and environmental disturbances. Experimental studies show statistically significant reductions in steady-state attitude error relative to standalone NMPC while maintaining robust behavior under uncertainty. The supervisory layer ensures graceful degradation to purely model-based control when the learning-based augmentation is unreliable.
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