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.

Conference

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

Fingerprint

Stochastic systems
Entropy
Probability density function
Controllers
White noise
Nonlinear systems

Keywords

  • entropy
  • control systems
  • stochastic systems

Cite this

Jia, J. F., Liu, T. Y., Yue, H., & Wang, H. (2006). Minimum entropy control algorithm for general dynamic stochastic systems. 368-372. Paper presented at First International Conference on Innovative Computing, Information and Control, 2006. ICICIC '06, Beijing, China. https://doi.org/10.1109/ICICIC.2006.114
Jia, J.F. ; Liu, T.Y. ; Yue, H. ; Wang, H. / Minimum entropy control algorithm for general dynamic stochastic systems. Paper presented at First International Conference on Innovative Computing, Information and Control, 2006. ICICIC '06, Beijing, China.5 p.
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keywords = "entropy , control systems, stochastic systems",
author = "J.F. Jia and T.Y. Liu and H. Yue and H. Wang",
year = "2006",
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note = "First International Conference on Innovative Computing, Information and Control, 2006. ICICIC '06 ; Conference date: 30-08-2006",

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Jia, JF, Liu, TY, Yue, H & Wang, H 2006, 'Minimum entropy control algorithm for general dynamic stochastic systems' Paper presented at First International Conference on Innovative Computing, Information and Control, 2006. ICICIC '06, Beijing, China, 30/08/06, pp. 368-372. https://doi.org/10.1109/ICICIC.2006.114

Minimum entropy control algorithm for general dynamic stochastic systems. / Jia, J.F.; Liu, T.Y.; Yue, H.; Wang, H.

2006. 368-372 Paper presented at First International Conference on Innovative Computing, Information and Control, 2006. ICICIC '06, Beijing, China.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Minimum entropy control algorithm for general dynamic stochastic systems

AU - Jia, J.F.

AU - Liu, T.Y.

AU - Yue, H.

AU - Wang, H.

PY - 2006

Y1 - 2006

N2 - 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.

AB - 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.

KW - entropy

KW - control systems

KW - stochastic systems

U2 - 10.1109/ICICIC.2006.114

DO - 10.1109/ICICIC.2006.114

M3 - Paper

SP - 368

EP - 372

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Jia JF, Liu TY, Yue H, Wang H. Minimum entropy control algorithm for general dynamic stochastic systems. 2006. Paper presented at First International Conference on Innovative Computing, Information and Control, 2006. ICICIC '06, Beijing, China. https://doi.org/10.1109/ICICIC.2006.114