SVM-based learning control of space robots in capturing operation

International Journal of Neural Systems
Panfeng Huang, Yangsheng Xu

Abstract

In this paper, we presents a novel approach for tracking and catching operation of space robots using learning and transferring human control strategies (HCS). We firstly use an efficient support vector machine (SVM) to parametrize the model of HCS. Then we develop a new SVM-based learning structure to better implement human control strategy learning in tracking and capturing control. The approach is fundamentally valuable in dealing with some problems such as small sample data and local minima, and so on. Therefore this approach is efficient in modeling, understanding and transferring its learning process. The simulation results attest that this approach is useful and feasible in generating tracking trajectory and catching objects autonomously.

References

Nov 1, 1994·World Journal of Microbiology & Biotechnology·J Michiels, J Vanderleyden

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Citations

Sep 5, 2009·International Journal of Neural Systems·Adnan Khashman
Oct 1, 2011·International Journal of Neural Systems·U Rajendra AcharyaJasjit S Suri
Mar 3, 2020·International Journal of Neural Systems·Huirang HouQinghao Meng
Jun 9, 2009·Neural Networks : the Official Journal of the International Neural Network Society·Hojjat Adeli, Ashif Panakkat

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