### 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

### Cite this

*International Journal of Automation and Computing*,

*2*(1), 67-74. https://doi.org/10.1007/s11633-005-0067-4

}

*International Journal of Automation and Computing*, vol. 2, no. 1, pp. 67-74. https://doi.org/10.1007/s11633-005-0067-4

**Linearized controller design for the output probability density functions of non-Gaussian stochastic systems.** / Kabore, P.; Baki, H.; Yue, H.; Wang, H.

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 -