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Volume 27 Issue 2
Feb.  2001
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Journal of Beijing University of Aeronautics and Astronautics  > 2001  >  27(2): 153-156.
HU Hong-jie, ER Lian-jie, LIU Qiang, et al. Study of GPS/MM Integrated Navigation System for Vehicle Positioning Based on D-S Evidence Reasoning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2001, 27(2): 153-156. (in Chinese)
Citation: HU Hong-jie, ER Lian-jie, LIU Qiang, et al. Study of GPS/MM Integrated Navigation System for Vehicle Positioning Based on D-S Evidence Reasoning[J]. Journal of Beijing University of Aeronautics and Astronautics, 2001, 27(2): 153-156. (in Chinese)
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Study of GPS/MM Integrated Navigation System for Vehicle Positioning Based on D-S Evidence Reasoning

  • Beijing University of Aeronautics and Astronautics, Dept. of Automatic Control

  • Received Date: 01 Nov 1999
  • Publish Date: 28 Feb 2001
    Abstract
  • Classic PID control method which is based on precise mathematical model has poor adaptivity and is not adaptive to nonlinear and time-variant plants.Conventional neural network is always complicated and its stability often suffers from the effect of initial weight value selecting.A simple stable direct adaptive PID control algorithm is proposed, which is based on RBF neural network.To guarantee the system stability and improve the system precision, initial weight value selecting problem for the neural network is discussed and corresponding iterative algorithm is provided. Simulation results indicate that the system robustness and tracking performance are superior to those of classic PID method.

     

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  • [1] Hunt K J, Sbarbaro D. Neural networks for control systems—a survey[J]. Automatica 1992, 28(6):1083~1112. [2] Robert M Sanner, Jean-Jacques E Slotine. Gaussian networks for direct adaptive control[J]. IEEE Trans on Neural Networks, 1992,3(6):837~863. [3] Carelli R, Camacho E F, Patifio D. A network based feedforward adaptive controller for robots[J]. IEEE Trans on Systems,Man,Cybernetics, 1995,25(9):1281~1287.
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