リアプノフ監視型物理情報ニューラルネットワーク補正層を用いた劣駆動宇宙機のMPC制御
MPC for underactuated spacecraft control with a Lyapunov supervised physics-informed neural network correction layer
劣駆動宇宙機の姿勢制御において、NMPCに物理情報ニューラルネットワークによる外乱推定とリアプノフ監視機構を組み合わせ、慣性不確かさや未モデル化結合に対する追従性能を向上させる階層的制御アーキテクチャを提案した。
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著者: Amirhossein Ayanmanesh Motlaghmofrad, Carlo Cena, Mauro Martini, Marcello Chiaberge
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
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. Monte Carlo 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.
