Minimum entropy control algorithm for general dynamic stochastic systems

J.F. Jia, T.Y. Liu, H. Yue, H. Wang

Research output: Contribution to conferencePaper

3 Citations (Scopus)

Abstract

In order to measure the uncertainty of the stochastic systems subjected to arbitrary noise disturbance instead of Gaussian white noise, the minimum entropy control of tracking errors for dynamic stochastic systems is presented in this paper. Different from conventional hypothesis, it is assumed that the system output and noise obey multi-to-one mapping, which is more general in the practical application. A controller design was described based on minimizing system output error entropy and a recursive optimization algorithm was set up for dynamic, non-Gaussian and nonlinear system. This approach only used the formula of the probability density function of the tracking error to calculate the controller and it did not need to know the style of the system model and the probability density function of noise, which often is difficult to measure in fact. An illustrative example is utilized to demonstrate the efficiency of the minimum entropy control algorithm and the approving simulation results have been gained.
Original languageEnglish
Pages368-372
Number of pages5
DOIs
Publication statusPublished - 2006
EventFirst International Conference on Innovative Computing, Information and Control, 2006. ICICIC '06 - Beijing, China
Duration: 30 Aug 2006 → …

Conference

ConferenceFirst International Conference on Innovative Computing, Information and Control, 2006. ICICIC '06
CountryChina
CityBeijing
Period30/08/06 → …

Keywords

  • entropy
  • control systems
  • stochastic systems

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