Adaptive Tracking Control for the Output PDFs Based on Dynamic Neural Networks

  • Yang Yi
  • Tao Li
  • Lei Guo
  • Hong Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4491)


In this paper, a novel adaptive tracking control strategy is established for general non-Gaussian stochastic systems based on two-step neural network models. The objective is to control the conditional PDF of the system output to follow a given target function by using dynamic neural network models. B-spline neural networks are used to model the dynamic output probability density functions (PDFs), then the concerned problem is transferred into the tracking of given weights corresponding to the desired PDF. The dynamic neural networks with undetermined parameters are employed to identify the nonlinear relationships between the control input and the weights. To achieve control objective, an adaptive state feedback controller is given to estimate the unknown parameters and control the nonlinear dynamics.


Probability Density Function Dynamic Neural Network Tracking Control Problem Compensation Term Conditional Probability Density Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Yang Yi
    • 1
  • Tao Li
    • 1
  • Lei Guo
    • 2
  • Hong Wang
    • 3
  1. 1.Research Institute of Automation, Southeast University, Nanjing 210096China
  2. 2.The School of Instrument Science and Opto-Electronics Engineering, Beihang University, Beijing 100083China
  3. 3.Control Systems Centre, The University of Manchester, ManchesterUK

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