基于物理的自适应Koopman模型预测姿态控制方法：用于存在动态不确定性的组合航天器
Physics-based Online Adaptive Koopman Model Predictive Attitude Control for Combined Spacecraft with Dynamic Uncertainties
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中文总结 AI 辅助
针对存在惯性不确定性的组合航天器姿态稳定问题，提出基于物理的自适应Koopman模型预测控制策略，通过二次规划降低计算负担，仿真验证了其有效性。
中文摘要 AI 辅助
本文针对存在惯性不确定性和主动目标机动性的组合航天器姿态稳定问题，提出了一种基于物理的自适应Koopman模型预测控制（MPC）策略。从四元数运动学推导得到一组解析提升函数，构建了一种新颖的基于四元数的Koopman模型，与传统的黑箱EDMD及基于高维DCM的模型相比，该模型能为非线性动力学提供更紧凑且物理解释性更强的线性表示。利用该标称模型的线性结构，采用基于梯度下降的更新律，可从实时输入输出数据中高效辨识时变惯性不确定性。将该自适应线性模型集成到MPC框架中，最优控制问题可简化为计算高效的二次规划（QP），与非线性自适应MPC相比，显著降低了在线计算负担。通过为MPC设计终端要素，正式确立了递归可行性和区域输入状态稳定性。在高保真三维模拟器中对组合航天器姿态稳定任务进行的对比仿真，验证了所提策略的有效性和优越性。
英文摘要
This paper proposes a physics-based adaptive Koopman Model Predictive Control (MPC) strategy for combined spacecraft attitude stabilization under inertia uncertainties and active target maneuverability. A novel, quaternion-based Koopman model is constructed from a set of analytical lifting functions derived from the quaternion kinematics, which provides a more compact and physically interpretable linear representation of the nonlinear dynamics compared with the conventional black-box EDMD and higher-dimensional DCM-based model. Leveraging the linear structure of this nominal model, a gradient descent-based update law is employed to efficiently identify time-varying inertial uncertainties from real-time input/output data. By integrating this adaptive linear model into the MPC framework, the optimal control problem reduces to a computationally efficient Quadratic Program (QP), thereby significantly lowering the online computational burden compared to nonlinear adaptive MPC. Recursive feasibility and regional input-to-state stability are formally established through the design of terminal ingredients for the MPC. The effectiveness and superiority of the proposed strategy are validated through comparative simulations of an attitude stabilization task for combined spacecraft in a high-fidelity 3D simulator.
发表机构
- Zhengzhou Advanced Research Institute, Harbin Institute of Technology（郑州先进研究院，哈尔滨工业大学）
- Department of Control Science and Engineering, Harbin Institute of Technology（控制科学与工程系，哈尔滨工业大学）
- School of Engineering, Newcastle University（纽卡斯尔大学工程学院）
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