Abstract
| Language | English |
|---|---|
| Pages | 67-74 |
| Number of pages | 8 |
| Journal | International Journal of Automation and Computing |
| Volume | 2 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jul 2005 |
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Keywords
- probability density function
- lyapunov stability theory
- B-splines neural networks
- dynamic stochastic systems
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Linearized controller design for the output probability density functions of non-Gaussian stochastic systems. / Kabore, P.; Baki, H.; Yue, H.; Wang, H.
In: International Journal of Automation and Computing, Vol. 2, No. 1, 07.2005, p. 67-74.Research output: Contribution to journal › Article
TY - JOUR
T1 - Linearized controller design for the output probability density functions of non-Gaussian stochastic systems
AU - Kabore, P.
AU - Baki, H.
AU - Yue, H.
AU - Wang, H.
PY - 2005/7
Y1 - 2005/7
N2 - 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.
AB - 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.
KW - probability density function
KW - lyapunov stability theory
KW - B-splines neural networks
KW - dynamic stochastic systems
UR - http://scholar.ilib.cn/A-gjzdhyjszz-e200501011.html
UR - http://link.springer.com/article/10.1007/s11633-005-0067-4
U2 - 10.1007/s11633-005-0067-4
DO - 10.1007/s11633-005-0067-4
M3 - Article
VL - 2
SP - 67
EP - 74
JO - International Journal of Automation and Computing
T2 - International Journal of Automation and Computing
JF - International Journal of Automation and Computing
SN - 1476-8186
IS - 1
ER -