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author:

Zhou, N. (Zhou, N..) [1] | Chen, R. (Chen, R..) [2] | Huang, J. (Huang, J..) [3] | Wen, G. (Wen, G..) [4]

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Abstract:

This paper discusses the problem of time-varying formation control for multiple spacecraft while achieving H ∞ performance. Firstly, an elaborate formation task function is designed by using estimator-based null-space based behavioral control method, and a predesigned desired velocity is calculated for each spacecraft. Then by employing backstepping technique associated with the universal approximation property of radial basis function neural networks (RBFNN), an adaptive H ∞ coordination control scheme is presented to drive all the spacecraft to implement the time-varying formation task in switching communication graph. Finally, rigorous theoretical analysis shows that the proposed algorithm implements the time-varying formation task successfully with H ∞ performance. © 2018 Technical Committee on Control Theory, Chinese Association of Automation.

Keyword:

H∞ Control; Neural Networks Control; Spacecraft System; Time-Varying Formation

Community:

  • [ 1 ] [Zhou, N.]Fujian Agriculture and Forestry University, College of Computer and Information Sciences, Fuzhou, 350002, China
  • [ 2 ] [Zhou, N.]University of Groningen, Faculty of Science and Engineering, Groningen, 9747AG, Netherlands
  • [ 3 ] [Chen, R.]Fujian Agriculture and Forestry University, College of Computer and Information Sciences, Fuzhou, 350002, China
  • [ 4 ] [Huang, J.]University of Groningen, Faculty of Science and Engineering, Groningen, 9747AG, Netherlands
  • [ 5 ] [Huang, J.]College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, 350108, China
  • [ 6 ] [Wen, G.]Binzhou University, College of Science, Shandong, 256600, China

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Source :

Chinese Control Conference, CCC

ISSN: 1934-1768

Year: 2018

Volume: 2018-July

Page: 7076-7081

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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