Statistic Tracking Control: A Multi-objective Optimization Algorithm

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)


This paper addresses a new type of control framework for dynamical stochastic systems, which is called statistic tracking control here. General non-Gaussian systems are considered and the tracked objective is the statistic information (including the moments and the entropy) of a given target probability density function (PDF), rather than a deterministic signal. The control is aiming at making the statistic information of the output PDFs to follow those of a target PDF. The B-spline neural network with modelling error is applied to approximate the corresponding dynamic functional. For the nonlinear weighting system with time delays in the presence of exogenous disturbances, the generalized H2 and H ∞  optimization technique is then used to guarantee the tracking, robustness and transient performance simultaneously in terms of LMI formulations.


Probability Density Function Tracking Problem Exogenous Disturbance Tracking Control Problem Gaussian System 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Lei Guo
    • 1
  1. 1.Research Institute of AutomationSoutheast UniversityNanjingChina

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