本文面向多臂航天器抓取非合作目标后的机动需求，针对所形成的组合体动力学参数未知问题，提出一种基于流形约束子空间辨识的积分式预测控制（Manifold-constraint-based Subspace Identification Integral Predictive Control, MSI-IPC），由基于流形约束的子空间辨识（MSI）方法与积分式预测控制（IPC）方法组成。通过收集多臂航天器历史输入-状态数据，MSI将系统状态方程参数的固有特征提炼为结构化约束，并以此构建约束流形，使辨识结果符合固有特征，提升状态量预测精度；IPC将控制输入增量视为预测控制方法的优化变量，从而降低控制输入抖振。MSI-IPC对任意开环构型的多臂航天器均有效，无需获取动力学参数信息，且有效解决传统隐式子空间预测控制（Implicit Subspace Predictive Con-trol, ISPC）方法面对噪声干扰时高维系统控制输入抖振、状态收敛难的问题。基于Lyapunov方法完成了受控闭环系统的输入-状态稳定（Input-to-State Stability, ISS）证明。数值仿真表明，在状态测量噪声存在时，相较于传统ISPC方法，MSI-IPC在提升状态量预测精度和降低控制输入抖振方面具有显著优势，充分验证MSI-IPC方法的实用性与控制性能。
This paper aims at realizing the post?operation maneuvering of multi?arm spacecraft after capturing non?cooperative targets. To solve the problem that the combination dynamic parameters are unknown, we propose a Manifold?constraint?based Subspace Identification Integral Predictive Control (MSI?IPC) method, which integrates a Manifold?constraint?based Subspace Identifica-tion (MSI) module with an Integral Predictive Control (IPC) scheme. By collecting historical input–state data of the multi-arm spacecraft, MSI extracts the inherent structural properties of the state equation parameters as structured constraints, thereby con-structing a constraint manifold that enforces adherence to system invariants and enhances state prediction accuracy. IPC utilizes the control input increments as optimization variables within the predictive control problem, effectively suppressing control in-put chattering. The resulting MSI?IPC method is applicable to multi?arm spacecraft in arbitrary open?loop configurations without requiring knowledge of any dynamic parameters. It overcomes the limitations of conventional implicit subspace predictive con-trol in high?dimensional, noisy systems, where input chattering often prevents convergence. A formal Input?to?State Stability (ISS) proof for the closed?loop system is provided via Lyapunov analysis. Numerical simulations under measurement noise demonstrate that MSI?IPC significantly surpasses traditional Indirect Subspace Predictive Control (ISPC) in both state prediction accuracy and control input smoothness, validating the method’s effectiveness and robustness.
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