Research on Orbiting Information Procession of Satellites Based on Parallel Management

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 215)

Abstract

The orbit attribution is the most important information on space object. Therefore, parallel management is channeled when complicated and systematic projects are decided and evaluated. Its method and process are applied to the study of system simulation. Traditionally, large-scale simulation and control of the system depended on single model simulation and traditional control approaches for making the target system under control at the only past time. However, the parallel management methods’ application will get the system under control and be accurate for a long time in the future. The paper briefly reviews ACP theory and data-based iterative learning control, then points out the necessity and feasibility of the combination of parallel control and iterative control by putting out the structure of NN iterative learning controller. The space objects prove the methods by calculating the orbit attribute at the end of the paper. This paper focuses on parallel management principles and makes further studies of its implementation methods for trying to offer some theory achievements and practical experiences for future researches.

Keywords

Parallel management Iterative control Learning control Space object information processing 

References

  1. 1.
    Wang FY (2007) Toward a paradigm shift in social computing: the ACP approach. IEEE Intell Syst 22(5):65–67Google Scholar
  2. 2.
    Wang FY (2004) Social computing: concepts, contents, and methods. Int J Intell Control Syst 9(2):91–96Google Scholar
  3. 3.
    Klinkrad H (1997) One year of conjunction events of ERS-1 and ERS-2 with objects of the USSPACECOM catalog. In: Proceeding of the 2nd European conference on space debris Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.The Academy of Equipment of PLABeijingChina

Personalised recommendations