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Volume 1 Issue 1
Jan.  2014

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2014  >  1(1): 101-112
Hongbin Ma, Yini Lv, Chenguang Yang and Mengyin Fu, "Decentralized Adaptive Filtering for Multi-agent Systems with Uncertain Couplings," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 1, pp. 101-112, 2014.
Citation: Hongbin Ma, Yini Lv, Chenguang Yang and Mengyin Fu, "Decentralized Adaptive Filtering for Multi-agent Systems with Uncertain Couplings," IEEE/CAA J. of Autom. Sinica, vol. 1, no. 1, pp. 101-112, 2014.
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Decentralized Adaptive Filtering for Multi-agent Systems with Uncertain Couplings

  • 1. School of Automation, Beijing Institute of Technology, Beijing 100081, China;

  • 2. Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology, Beijing 100081, China;

  • 3. School of Automation, Beijing Institute of Technology, Beijing 100081, China;

  • 4. School of Automation, Beijing Institute of Technology, Beijing 100081, China;

  • 5. School of Computing and Mathematics, Plymouth University, Plymouth PL4 8AA, UK;

  • 6. School of Automation, Beijing Institute of Technology, Beijing 100081, China

Funds:

This work was supported by National Natural Science Foundation of China (NSFC) (61004059, 61004139, 61031001), China-UK NSFC-RS Joint Project (61211130359), the EU Marie Curie Project (PIIFR-GA-20 10-910078- H2R), Beijing Outstanding Talents Programme (2012D00 901 1000003), and Graduate Teaching/Innovation Funding of Beijing Institute of Technology.

    Abstract
  • In this paper, the problem of decentralized adaptive filtering for multi-agent systems with uncertain couplings is formulated and investigated. This problem is challenging due to the mutual dependency of state estimation and coupling estimation. First, the problem is divided into four typical types based on the origin of coupling relations and linearity of the agent dynamics. Then models of the four types are given and the corresponding decentralized adaptive filtering algorithms are designed for the purpose of estimation of the unknown states and couplings which denotes the relations between agents and their neighbor agents in terms of states or outputs simultaneously, with preliminary stability analysis and discussions. For testing the effects of algorithm, with the so-called certainty-equivalence principle, control signals are designed based on the results of state estimation and coupling estimation got by the proposed decentralized adaptive filtering algorithms. Extensive simulations are conducted to verify the effectiveness of considered algorithms.

     

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    • References(29)
  • [1]
    Yan Wei-Sheng, Li Jun-Bing, Wang Yin-Tao. Consensus for damaged multi-agent system. Acta Automatica Sinica, 2012, 38(11):1880-1884(in Chinese)
    [2]
    Yu Hong-Wang, Zheng Yu-Fan. Dynamic behavior of multi-agent systems with distributed sampled control. Acta Automatica Sinica, 2012, 38(3):357-365(in Chinese)
    [3]
    Lukomski R, Wilkosz K. Modeling of multi-agent system for power system topology verification with use of petri nets. In:Proceedings of the 2010 Modern Electric Power Systems. Wroclaw, Poland:IEEE, 2010. 1-6
    [4]
    Ma H B, Ge S S, Lum K Y. WLS-based partially decentralized adaptive control for coupled ARMAX multi-agent dynamical system. In:Proceedings of the 2009 American Control Conference. Piscataway, NJ, United States:IEEE, 2009. 3543-3548
    [5]
    Ma H B. Decentralized adaptive synchronization of a stochastic discretetime multi-agent dynamic model. SIAM Journal of Control and Optimization, 2009, 48(2):859-880
    [6]
    Ma H B, Yang C G, Fu M Y. Discrete Time Systems. Vienna:I-Tech Education and Publishing, 2011. 229-254
    [7]
    Al-Kanhal T, Abbod M. Multi-agent system for dynamic manufacturing system optimization. In:Proceedings of the 8th International Conference of Computational Science. Berlin, Germany:Springer Verlag, 2008. 634-643
    [8]
    Luo Xiao-Yuan, Shao Shi-Kai, Guan Xin-Ping, Zhao Yuan-Jie. Dynamic generation and control of optimally persistent formation for multi-agent systems. Acta Automatica Sinica, 2013, 39(9):1431-1438(in Chinese)
    [9]
    Hall E L, Alhaj Ali S M, Ghaffari M. Engineering robust intelligent robots. In:Proceedings of the 2010 SPIE-The International Society for Optical Engineering. USA:SPIE-The International Society for Optical Engineering, 2010. 753904-753918
    [10]
    Wang P, Jiang H L, Shi W Z, Cheng M C. Design and realization of remote control in smart home system. In:Proceedings of the 2009 International Conference on Communication Software and Networks. Macau, China:IEEE, 2009. 13-15
    [11]
    Noor A K, Venneri S L. Intelligent synthesis environment for future aerospace systems. IEEE Aerospace and Electronic Systems Magazine, 2008, 23(4):31-44
    [12]
    Yang Z S, Sun J P, Yang C X. Multi-agent urban expressway control system based on generalized knowledge-based model. In:Proceedings of the 2003 IEEE International Conference on Intelligent Transportation Systems. Shanghai, China:IEEE, 2003. 1759-1763
    [13]
    Kong Xiang-Jie, Shen Guo-Jiang, Sun You-Xian. Dynamic intelligent coordinated control with arterial traffic based on multi-agent. Journal of PLA University of Science and Technology (Natural Science Edition), 2010, 11(5):544-550(in Chinese)
    [14]
    Olfati-Saber R, Murray R. Consensus problems in networks of agents with switching topology and time delays. IEEE Transactions on Automatic Control, 2004, 49(9):1520-1533
    [15]
    Ren W. Decentralization of coordination variables in multi-vehicle systems. In:Proceedings of the 2006 IEEE International Conference on Networking, Sensing and Control. Ft. Lauderdale, FL:IEEE, 2006. 550-555
    [16]
    Ren W, Moore K, Chen Y Q. High-order consensus algorithms in cooperative vehicle systems. In:Proceedings of the 2006 IEEE International Conference on Networking, Sensing and Control. Ft. Lauderdale, FL:IEEE, 2006. 475-462
    [17]
    Cao Y C, Ren W. Distributed containment control for multiple autonomous vehicles with double-integrator dynamics:Algorithms and experiments. IEEE Transactions on Control Systems Technology, 2011, 19(4):929-938
    [18]
    Kalman R E. A new approach to linear filtering and prediction problems. ASME Journal of Basic Engineering, 1960, 82:34-45
    [19]
    Simon D. Optimal State Estimation. New Jersey, America:John Wiley & Sons, Inc., 2006.
    [20]
    Wang J, Liang Q H, Liang K, Wei S Q. A new extended Kalman filter based carrier tracking loop. In:Proceedings of the 3rd IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications. Beijing, China:IEEE, 2009. 1181-1184
    [21]
    Wu Yao, Luo Xiong-Lin. Robustness analysis of Kalman filtering algorithm for multi-rate systems. Acta Automatica Sinica, 2012, 38(2):156-174(in Chinese)
    [22]
    Wen Tao, Ge Quan-Bo. Research on real-time block Kalman filtering estimation methods for the un-modeled system based on output measurements. Acta Electronica Sinica, 2012, 40(10):1958-1964(in Chinese)
    [23]
    Xiong Jie, Guan Wei, Sun Yu-Xing. Metro transfer passenger forecasting based on Kalman filter. Journal of Beijing Jiaotong University, 2013, 37(3):112-116, 121(in Chinese)
    [24]
    Liu Zhou-Zhou. Multi-sensor information fusion and its application. Electronic Design Engineering, 2013, 21(11):116-123(in Chinese)
    [25]
    Ma H B, Zhao Y L, Fu M Y, Yang C G. Decentralized adaptive control for a class of semi-parametric uncertain multi-agent systems. In:Proceedings of the 10th World Congress on Intelligent Control and Automation. Beijing, China:IEEE, 2012. 2060-2065
    [26]
    Quine B M. A derivative-free implementation of the extended Kalman filter. Automatica, 2006, 42(11):1927-1934
    [27]
    Jazwinski A J. Stochastic Process and Filter Theory. New York:Academic Press, 1970.
    [28]
    Solo V. Stability of the Kalman filter with stochastic time-varying parameters. In:Proceedings of the 35th IEEE Conference on Decision and Control. Kobe, Japan:IEEE, 1996. 57-61
    [29]
    Reif K, Gunther S, Yaz E, Unbehauen R. Stochastic stability of the discrete-time extended Kalman filter. Automatic Control, 1999, 44(4):714-728
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