Dynamics-based deep learning and fractional-order fixed-time sliding mode control for space robot impedance system

Abstract

To complete complex tasks with space robots in the impedance environment, sliding mode control (SMC) has been proposed and plays an important role in the past decades. Learning-based control has also met with great success in robotics and has been closely followed in the control of space robots. However, as an interdisciplinary, only a few studies have combined deep learning and traditional controller in the space robot directly. This paper proposes a novel fractional-order fixed-time SMC for impedance control of space robots, taking advantage of a model-based deep learning approach to obtain the dynamics of space robots. The learning results are used to make predictions for the data-based robot dynamics. The fractional-order fixed-time SMC controller is added to that data-based model, and it ensures the finite closed-loop convergence time. Small-dataset training is more reasonable in the situation of space robots, which reduces training time and sampling size. Compared to traditional methods, combining deep learning and fractional-order fixed-time SMC provides better tracking performance and improves closed-loop robustness. It accomplishes the control target in the impedance model using the estimated data-driven parameters. Theoretical analysis proves the closed-loop behavior, and simulation results verify its effectiveness.

Keywords

Space robot impedance control deep learning fixed-time sliding mode control

Get full access to this article

View all access options for this article.

References

Liu

Cui

Liu

, et al. Robotic hand-arm system for on-orbit servicing missions in tiangong-2 space laboratory. Bingley: Emerald Group, 2019.

Flores-Abad

Pham

, et al. A review of space robotics technologies for on-orbit servicing. Prog Aerosp Sci 2014; 68: 1–26, DOI: 10.1016/j.paerosci.2014.03.002

Liu

Wang

Tang

, et al. Probabilistic ensemble neural network model for long-term dynamic behavior prediction of free-floating space manipulators. Aerosp Sci Technol 2021; 119: 107138, DOI: 10.1016/j.ast.2021.107138

Jin

Rocco

Geng

. Cartesian trajectory planning of space robots using a multi-objective optimization. Aerosp Sci Technol 2021; 108: 106360.

Chen

Guhl

. Industrial robot control with object recognition based on deep learning. Procedia CIRP 2018; 76: 149–154.

She

Xin

. Quantum-interference artificial neural network with application to space manipulator control. IEEE Trans Aerosp Electron Syst 2021; 57(4): 2167–2182. DOI: 10.1109/TAES.2021.3053124

Stolfi

Angeletti

Gasbarri

, et al. A deep learning strategy for on-orbit servicing via space robotic manipulator. Aerotec Missili Spaz 2019; 98(4): 273–282.

Ulrich

Sasiadek

. Extended kalman filtering for flexible joint space robot control. In: Proceedings of the 2011 American control conference, San Francisco, CA, 29 June–01 July 2011; IEEE; pp. 1021–1026.

Yao

, et al. Natural human–robot interface using adaptive tracking system with the unscented kalman filter. IEEE Trans Hum Mach Syst 2020; 50(1): 42–54. DOI: 10.1109/THMS.2019.2947576

10.

Zhang

Tang

, et al. Dynamic visual tracking for robot manipulator using adaptive fading kalman filter. IEEE Access 2020; 8: 35113–35126. DOI: 10.1109/ACCESS.2020.2973299

11.

Loo

Tan

Nurzaman

. H-infinity based extended kalman filter for state estimation in highly non-linear soft robotic system. In: 2019 American control conference (ACC), Philadelphia, PA, USA, 10–12 July 2019; pp. 5154–5160. DOI: 10.23919/ACC.2019.8814869

12.

Huang

Yuan

Liang

. Adaptive sliding-mode control of space robot during manipulating unknown objects. In: 2007 IEEE international conference on control and automation. Guangzhou, China, 30 May–01 June 2007; IEEE; pp. 2907–2912.

13.

Kobayashi

Tsuda

. Sliding mode control of space robot for unknown target capturing. Engineering Letters 2011; 19(2): 105.

14.

Arisoy

Bayrakceken

Basturk

, et al. High order sliding mode control of a space robot manipulator. In: Proceedings of 5th international conference on recent advances in space technologies-RAST2011, Istanbul, Turkey, 09–11 June 2011; IEEE; pp. 833–838.

15.

Soltanpour

Khooban

Soltani

. Robust fuzzy sliding mode control for tracking the robot manipulator in joint space and in presence of uncertainties. Robotica 2014; 32(3): 433–446.

16.

Yan

Liu

Lan

, et al. Trajectory planning and low-chattering fixed-time nonsingular terminal sliding mode control for a dual-arm free-floating space robot. Robotica 2022; 40(3): 625–645.

17.

Uyama

Nakanishi

Nagaoka

, et al. Impedance-based contact control of a free-flying space robot with a compliant wrist for non-cooperative satellite capture. In: 2012 IEEE/RSJ international conference on intelligent robots and systems, Vilamoura-Algarve, Portugal, 07–12 October 2012; pp. 4477–4482. DOI: 10.1109/IROS.2012.6386082

18.

Uyama

Narumi

. Hybrid impedance/position control of a free-flying space robot for detumbling a noncooperative satellite. IFAC-PapersOnLine 2016; 49(17): 230–235.

19.

Huang

Kuang

. Fuzzy approximate learning-based sliding mode control for deploying tethered space robot. IEEE Trans Fuzzy Syst 2021; 29(9): 2739–2749.

20.

Chen

Yang

Liu

. Rbfnn-based nonsingular fast terminal sliding mode control for robotic manipulators including actuator dynamics. Neurocomputing 2019; 362: 72–82. DOI: 10.1016/j.neucom.2019.06.083. URL https://www-sciencedirect-com-443.web.bisu.edu.cn/science/article/pii/S092523121930997X

21.

Liang

Wang

Zhang

. Adaptive nonsingular terminal sliding mode control for rehabilitation robots. Comput. Electr. Eng 2022; 99: 107718. DOI: 10.1016/j.compeleceng.2022.107718. URL https://www-sciencedirect-com-443.web.bisu.edu.cn/science/article/pii/S0045790622000349

22.

de Wit

Siciliano

Bastin

. Theory of robot control. Cham, Switzerland: Springer Science and Business Media, 2012.

23.

Lutter

Ritter

Peters

. Deep Lagrangian networks: using physics as model prior for deep learning. arXiv preprint arXiv:190704490, 2019.

24.

Sierociuk

Dzieliński

. Fractional kalman filter algorithm for the states, parameters and order of fractional system estimation. Warsaw, Poland: International Journal of Applied Mathematics and Computer Science, 2006.

25.

Polyakov

. Nonlinear feedback design for fixed-time stabilization of linear control systems. IEEE Trans Automat Contr 2012; 57(8): 2106–2110.