ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Data-driven predictive control for multi-arm spacecraft after target capture
Received date: 2025-08-18
Revised date: 2025-10-16
Accepted date: 2025-12-31
Online published: 2026-01-15
Supported by
Young Scientists Fund of the National Natural Science Foundation of China （Category C）(12302055);Research Start-up Funds of Hangzhou International Innovation Institute of Beihang University of China(2024KQ089);State Key Laboratory of Micro-Spacecraft Rapid Design and Intelligent Cluster(MS0124107)
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.
Bicheng CAI , Siming ZHANG , Xiaozhe JU , Yuan LI , Yong ZHAO . Data-driven predictive control for multi-arm spacecraft after target capture[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2026 , 47(14) : 632687 -632687 . DOI: 10.7527/S1000-6893.2025.32687
| [1] | CHEN G， WANG Y Q， WANG Y F， et al. Detumbling strategy based on friction control of dual-arm space robot for capturing tumbling target［J］. Chinese Journal of Aeronautics， 2020， 33（3）： 1093-1106. |
| [2] | 徐升， 褚明， 蔺绍奇， 等. 非合作目标软捕获后的动力学参数无激励辨识［J］. 航空学报， 2023， 44（19）： 228342. |
| XU S， CHU M， LIN S Q， et al. Dynamic parameter identification without excitation for non-cooperative targets post soft capture［J］. Acta Aeronautica et Astronautica Sinica， 2023， 44（19）： 228342 （in Chinese）. | |
| [3] | 夏鹏程， 罗建军， 王明明， 等. 空间双臂机器人抓捕非合作目标后的协调稳定控制［J］. 航空学报， 2022， 43（2）： 325398. |
| XIA P C， LUO J J， WANG M M， et al. Coordinated stabilization control for dual-arm space robot capturing a non-cooperative target［J］. Acta Aeronautica et Astronautica Sinica， 2022， 43（2）： 325398 （in Chinese）. | |
| [4] | LU L， YUE C F， SHEN Q， et al. Hierarchical passivity-based force-position-configuration coordinated control of multi-branch spacecraft［J］. IEEE Transactions on Aerospace and Electronic Systems， 2025， 61（2）： 4223-4237. |
| [5] | YUE C F， LIN T， ZHANG X， et al. Hierarchical path planning for multi-arm spacecraft with general translational and rotational locomotion mode［J］. Science China Technological Sciences， 2023， 66（4）： 1180-1191. |
| [6] | YANG Z Y， LAI M Z， QI J， et al. Reorientation and obstacle avoidance control of free-floating modular robots using sinusoidal oscillator［J］. Chinese Journal of Aeronautics， 2024， 37（6）： 262-275. |
| [7] | 赵浩天， 邱实， 刘明， 等. 基于全生命周期数据驱动的通讯卫星行波管退化评估方法［J］. 航空学报， 2025， 46（12）： 331376. |
| ZHAO H T， QIU S， LIU M， et al. Degradation evaluation method for communication satellite traveling wave tubes based on full life cycle data［J］. Acta Aeronautica et Astronautica Sinica， 2025， 46（12）： 331376 （in Chinese）. | |
| [8] | SUN C， YUAN J P， ZHU Z X. Neural adaptive control for a ground experiment of the space proximity operation in a six-degree-of-freedom micro-gravity simulation system［J］. Chinese Journal of Aeronautics， 2020， 33（9）： 2420-2433. |
| [9] | WANG D， GAO N， LIU D R， et al. Recent progress in reinforcement learning and adaptive dynamic programming for advanced control applications［J］. IEEE/CAA Journal of Automatica Sinica， 2024， 11（1）： 18-36. |
| [10] | WU Y H， YU Z C， LI C Y， et al. Reinforcement learning in dual-arm trajectory planning for a free-floating space robot［J］. Aerospace Science and Technology， 2020， 98： 105657. |
| [11] | SONG B Y， LI J Q， LIU X Y， et al. A trajectory planning method for capture operation of space robotic arm based on deep reinforcement learning［J］. Journal of Computing and Information Science in Engineering， 2024， 24（9）： 091003. |
| [12] | SUN Q， JIA Y M. Adaptive dynamic programming-based tracking control for free-floating space manipulators［C］∥ 2024 IEEE 19th Conference on Industrial Electronics and Applications （ICIEA）. Piscataway： IEEE Press， 2024： 1-6. |
| [13] | ZHANG Z G， MA T H， ZHAO Y D， et al. Adaptive dynamic programming-based multi-fault tolerant control of reconfigurable manipulator with input constraint［J］. Complex & Intelligent Systems， 2024， 10（6）： 8341-8353. |
| [14] | ZHANG Y H， ZOU L， LIU Y， et al. A brief survey on nonlinear control using adaptive dynamic programming under engineering-oriented complexities［J］. International Journal of Systems Science， 2023， 54（8）： 1855-1872. |
| [15] | SASSELLA A， BRESCHI V， FORMENTIN S. Data-driven design of explicit predictive controllers［C］∥ 2022 IEEE 61st Conference on Decision and Control （CDC）. Piscataway： IEEE Press， 2023： 2821-2826. |
| [16] | DE PERSIS C， TESI P. Formulas for data-driven control： stabilization， optimality， and robustness［J］. IEEE Transactions on Automatic Control， 2020， 65（3）： 909-924. |
| [17] | HUANG L B， COULSON J， LYGEROS J， et al. Decentralized data-enabled predictive control for power system oscillation damping［J］. IEEE Transactions on Control Systems Technology， 2022， 30（3）： 1065-1077. |
| [18] | BERBERICH J， K?HLER J， MüLLER M A， et al. Linear tracking MPC for nonlinear systems： Part Ⅱ： The data-driven case［J］. IEEE Transactions on Automatic Control， 2022， 67（9）： 4406-4421. |
| [19] | ZHANG X W， HAN M H， YIN X Y. Reduced-order Koopman modeling and predictive control of nonlinear processes［J］. Computers & Chemical Engineering， 2023， 179： 108440. |
| [20] | KADALI R， HUANG B， ROSSITER A. A data driven subspace approach to predictive controller design［J］. Control Engineering Practice， 2003， 11（3）： 261-278. |
| [21] | BRESCHI V， HAMDAN T BOU， MERCèRE G， et al. Tuning of subspace predictive controls［J］. IFAC-PapersOnLine， 2023， 56（3）： 103-108. |
| [22] | YE H， LIU Y T. Online recursive closed-loop state space model identification for damping control［C］∥ 2010 Asia-Pacific Power and Energy Engineering Conference. Piscataway： IEEE Press， 2010： 1-4. |
| [23] | KHALIL H K. Nonlinear Systems［M］. 3rd ed. Upper Saddle River： Prentice Hall， 2002： 144. |
| [24] | CAI B C， WEI C， YUE C F， et al. Artificial potential incorporated adaptive fixed-time sliding mode control for target capture［J］. Advances in Space Research， 2023， 72（4）： 982-996. |
| [25] | 蔡璧丞. 空间非合作目标包络式抓捕优化与控制［D］. 哈尔滨： 哈尔滨工业大学， 2024： 26. |
| CAI B C. Research on optimization and control of non-cooperative target envelope capture［D］. Harbin： Harbin Institute of Technology， 2024： 26 （in Chinese） . | |
| [26] | GRüNE L， PANNEK J， SEEHAFER M， et al. Analysis of unconstrained nonlinear MPC schemes with time varying control horizon［C］∥ 2012 IEEE 51st IEEE Conference on Decision and Control （CDC）. Piscataway： IEEE Press， 2013： 2605-2610. |
| [27] | GRUNE L， RANTZER A. On the infinite horizon performance of receding horizon controllers［J］. IEEE Transactions on Automatic Control， 2008， 53（9）： 2100-2111. |
/
| 〈 |
|
〉 |
CJA