Impedance control of space manipulator based on improved neural network

doi:10.16708/j.cnki.1000-758X.2022.0025

Chinese Space Science and Technology ›› 2022, Vol. 42 ›› Issue (2): 82-90.doi: 10.16708/j.cnki.1000-758X.2022.0025

Previous Articles Next Articles

Impedance control of space manipulator based on improved neural network

QI Yifan , JIA Yinghong, ZHAO Baoshan, ZHONG Rui, HONG Wenqing

1Kunming Institute of Physics, Kunming 650223，China
2School of Astronautics, Beihang University, Beijing 100191, China
3Tianjin Key Laboratory of Microgravity and Hypogravity Environment Simulation Technology, Tianjin Institute of Aerospace Mechanical and Electrical Equipment, Tianjin 300301, China

Published:2022-04-25 Online:2022-03-30

Abstract

Abstract: An impedance control method based on improved neural network was proposed oriented to compliance control of space manipulators under the condition of uncertain environmental information and unknown collision model. Based on the closed-loop equation of impedance control system, the reasons why precise force control can’t be achieved under the condition of uncertain environmental information and unknown collision model were analyzed. The weight matrices in the neural network were adjusted by particle swarm optimization algorithm to improve the convergence speed and optimization performance of neural network. An impedance controller based on the improved neural network was proposed, which accomplished compliance control. The improved neural network can adjust the impedance parameters on line to achieve better compliance control effect. Numerical simulation results show that the proposed controller can reduce the force control error and position control error effectively, and has a better anti-jamming capability for force feedback interference signal than traditional impedance controller.

Key words: space manipulator, impedance control, neural network, particle swarm optimization, intelligent control

QI Yifan , JIA Yinghong, ZHAO Baoshan, ZHONG Rui, HONG Wenqing. Impedance control of space manipulator based on improved neural network[J]. Chinese Space Science and Technology, 2022, 42(2): 82-90.

0
/ / Recommend

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: http://journal26.magtechjournal.com/kjkxjs/EN/10.16708/j.cnki.1000-758X.2022.0025

http://journal26.magtechjournal.com/kjkxjs/EN/Y2022/V42/I2/82

[1]	ZHU Chao, ZHANG Wenming, HU Chengwei, TANG Zixin, LIANG Changchun, KONG Xu, LI Delun, WANG Youyu, ZENG Lei, ZHANG Xinrui. Maintenance system design and verification for space station manipulator end-effector [J]. Chinese Space Science and Technology, 2023, 43(5): 79-86.
[2]	ZHANG Chengyue, QUAN Ronghui, ZHANG Haicheng. Dielectric surface charging potential inversion method based on BP neural network [J]. Chinese Space Science and Technology, 2022, 42(6): 134-139.
[3]	ZHOU Hongtao, FANG Meihua. ANN inversion model for electron flux based on deep charging [J]. Chinese Space Science and Technology, 2022, 42(5): 57-64.
[4]	XIAO Aiqun, JIANG Hongxiang. Booster pose estimation based on 3D point cloud reconstruction [J]. Chinese Space Science and Technology, 2022, 42(3): 74-81.
[5]	YUAN Wei, XU Wenbo, ZHOU Tian. Remote sensing image segmentation based on PSPNet with neighborhood color difference [J]. Chinese Space Science and Technology, 2022, 42(1): 125-130.
[6]	NI Ping, WEN Xin. Fault diagnosis of satellite attitude actuator based on recurrent neural network [J]. Chinese Space Science and Technology, 2021, 41(4): 121-126.
[7]	NING Dongpo, XU Zhiming, LIU Zhijia. Temperature estimation of satellite equipment without thermistor based on BP neural network#br# [J]. Chinese Space Science and Technology, 2021, 41(2): 48-54.
[8]	WANG Hao, ZHANG Zhanyue, ZHANG Haitao, JIANG Ping. Reconstruction method of regional reconnaissance elastic constellation based on improved MOPSO#br# [J]. Chinese Space Science and Technology, 2021, 41(2): 86-95.
[9]	PENG Yaqi, LI Chonghui, WANG Yiwen, WEI Wulei, DING Baichao, LIU Yangqian. Error analysis and prediction of Klobuchar ionospheric model [J]. Chinese Space Science and Technology, 2021, 41(1): 48-54.
[10]	WANG Mengfei, ZHANG Jun. Multi-objective robust attitude control via DPSO algorithm for flexible spacecraft [J]. Chinese Space Science and Technology, 2021, 41(1): 64-74.
[11]	SUN Yibin, WANG Limei. Design of controller for gravity unloading system based on neural network [J]. Chinese Space Science and Technology, 2021, 41(1): 113-119.
[12]	GENG Feilong, LI Shuang, HUANG Xuxing, YANG Bin, CHANG Jiansong, LIN Bo. Fault diagnosis and fault tolerant control of spacecraft attitude control system via deep neural network [J]. Chinese Space Science and Technology, 2020, 40(6): 1-12.
[13]	GUO Chengjun, PANG Guoqiang. Ionospheric VTEC fusion based on BP neural network technology [J]. Chinese Space Science and Technology, 2020, 40(4): 78-83.
[14]	WANG Ran, ZHOU Zhicheng, QU Guangji, CHEN Yujun. Sliding mode control based on fuzzy neural network for variable structure spacecraft [J]. Chinese Space Science and Technology, 2020, 40(3): 56-63.
[15]	JIN Chendi, KANG Guohua, GUO Yujie, QIAO Siyuan. Onorbit intelligent identification of combined spacecraft′s inertia parameter based on deep learning [J]. , 2019, 39(2): 1-12.

Impedance control of space manipulator based on improved neural network

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments