多臂航天器抓捕目标后的数据驱动预测控制

doi:10.7527/S1000-6893.2025.32687

Abstract

Abstract:

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 Identification （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 constructing 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 input 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 indirect subspace predictive control 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 conventional Indirect Subspace Predictive Control （ISPC） in both state prediction accuracy and control input smoothness， validating the method’s effectiveness and robustness.

Key words: multi-arm spacecraft, non-cooperative target, manifold constraint identification, integral predictive control, input-to-state stable

CLC Number:

V448.2

Bicheng CAI, Siming ZHANG, Xiaozhe JU, Yuan LI, Yong ZHAO. Data-driven predictive control for multi-arm spacecraft after target capture[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(14): 632687.

Figures/Tables 16

Fig.1

Table 1

Fig.2

Table 2

Parameters of multi-arm spacecraft

参数	数值
基座尺寸/m	1.2×0.6×0.6
臂杆长度/m	0.4
基座质量/kg	5
臂杆质量/kg	0.2
基座惯量/（kg·m²）	$0.5 d i a g (1,1, 1)$
臂杆惯量/（kg·m²）	$0.02 d i a g (0.01,1, 1)$
末端组合杆件质量/kg	2
末端组合杆件惯量/（kg·m²）	$0.2 d i a g (0.01,1, 1)$

Table 2

Table 3

Parameters of MSI-IPC

参数	数值
状态量权重矩阵 $Q$	$100 d i a g (1 1 × 12, 0.1 × 1 1 × 12)$
控制输入权重矩阵 $W$	$0.01 d i a g (1 1 × 12)$
控制输入增量权重矩阵 $S$	$d i a g (0.1 × 1 1 × 3, 1 1 × 9)$
预测视界 $p$	10
基座喷管最大输入/N	1
基座飞轮最大输入/（N·m）	1
关节电机最大输入/（N·m）	1
仿真时间步长 $h$ /s	0.01
历史数据长度 $T$	100

Table 3

Fig.3

Fig.4

Fig.5

Table 4

Fig.6

Fig.7

Fig.8

Table 5

Fig.9

Fig.10

Fig.11

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对比维度	MSI	MII
搜索方法	解析法	迭代法
搜索步数	单步	多步
优化问题特性	凸优化	非凸优化
最优性保证	全局最优	全局次优
优化约束	无约束	有约束
计算消耗	小	大

稳态性能/控制方法	PD	ISPC	ISIPC	MSI-IPC
基座位置/m	0.055 5	0.061 9	0.248	0.285×10^-3
基座姿态角/（°）	1.07	1.15	4.88	0.798×10^-3
关节角/（°）	1.18	1.45	13.3	0.012 8
喷管控制输入/N	0.855	0.569	0.827	0.008 60
飞轮控制输入/（N·m）	0.064	0.276	0.558	0.009 32
关节电机输入/（N·m）	0.022 9	0.159	0.225	0.00 947

稳态性能/控制方法	MIPC	MSI-IPC
基座位置/mm	0.131	0.145
基座姿态角/（10^-3（°））	1.217	1.256
关节角/（10^-3（°））	21.610	21.548
喷管控制输入/（10^-3 N）	8.447	8.442
飞轮控制输入/（10^-3 N·m）	9.369	9.365
关节电机控制输入/（10^-3 N·m）	9.909	9.910

Data-driven predictive control for multi-arm spacecraft after target capture

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