Linearized controller design for the output probability density functions of non-Gaussian stochastic systems

P. Kabore, H. Baki, H. Yue, H. Wang

Research output: Contribution to journalArticle

Abstract

This paper presents a linearized approach for the controller design of the shape of output probability density functions for general stochastic systems. A square root approximation to an output probability density function is realized by a set of B-spline functions. This generally produces a nonlinear state space model for the weights of the B-spline approximation. A linearized model is therefore obtained and embedded into a performance function that measures the tracking error of the output probability density function with respect to a given distribution. By using this performance function as a Lyapunov function for the closed loop system, a feedback control input has been obtained which guarantees closed loop stability and realizes perfect tracking. The algorithm described in this paper has been tested on a simulated example and desired results have been achieved.
Original languageEnglish
Pages (from-to)67-74
Number of pages8
JournalInternational Journal of Automation and Computing
Volume2
Issue number1
DOIs
Publication statusPublished - Jul 2005

Keywords

  • probability density function
  • lyapunov stability theory
  • B-splines neural networks
  • dynamic stochastic systems

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