Radial basis function based iterative learning control for stochastic distribution systems

Hong Wang; Puya Afshar

doi:10.1109/ISIC.2006.285585

Radial basis function based iterative learning control for stochastic distribution systems

Research output: Contribution to conference › Other

Abstract

In this paper, an Iterative Learning Control (ILC) scheme is presented for the control of the shape of the output probability density functions (PDF) for a class of stochastic systems in which the relationship between approximation basis functions and the control input is linear, and the stochastic system is not necessarily Gaussian. A Radial Basis Function Neural Network (RBFNN) has been employed for the output PDF approximation and the coefficients of the approximation are linearly related to the control input. A three-stage method for the ILC-based PDF control is proposed which incorporates a) identifying PDF model parameters; b) calculating the control input; and c) updating RFBN parameters. The latter is accomplished based on P-type ILC law and the difference of the desired and calculated output PDF within a batch. Conditions for the convergent ILC rules have been derived. Simulation results are included to demonstrate the effectiveness of proposed method. © 2006 IEEE.

Original language	English
Pages	100-105
Number of pages	5
DOIs	https://doi.org/10.1109/ISIC.2006.285585
Publication status	Published - 2006
Event	Joint 2006 IEEE Conference on Control Applications (CCA), Computer-Aided Control Systems Design Symposium (CACSD) and International Symposium on Intelligent Control (ISIC) - Munich Duration: 1 Jul 2006 → …

Conference

Conference	Joint 2006 IEEE Conference on Control Applications (CCA), Computer-Aided Control Systems Design Symposium (CACSD) and International Symposium on Intelligent Control (ISIC)
City	Munich
Period	1/07/06 → …

Keywords

Iterative learning mechanism
Probability density functions
RBF neural networks
Stochastic systems

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10.1109/ISIC.2006.285585

Cite this

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title = "Radial basis function based iterative learning control for stochastic distribution systems",

abstract = "In this paper, an Iterative Learning Control (ILC) scheme is presented for the control of the shape of the output probability density functions (PDF) for a class of stochastic systems in which the relationship between approximation basis functions and the control input is linear, and the stochastic system is not necessarily Gaussian. A Radial Basis Function Neural Network (RBFNN) has been employed for the output PDF approximation and the coefficients of the approximation are linearly related to the control input. A three-stage method for the ILC-based PDF control is proposed which incorporates a) identifying PDF model parameters; b) calculating the control input; and c) updating RFBN parameters. The latter is accomplished based on P-type ILC law and the difference of the desired and calculated output PDF within a batch. Conditions for the convergent ILC rules have been derived. Simulation results are included to demonstrate the effectiveness of proposed method. {\textcopyright} 2006 IEEE.",

keywords = "Iterative learning mechanism, Probability density functions, RBF neural networks, Stochastic systems",

author = "Hong Wang and Puya Afshar",

year = "2006",

doi = "10.1109/ISIC.2006.285585",

language = "English",

pages = "100--105",

note = "Joint 2006 IEEE Conference on Control Applications (CCA), Computer-Aided Control Systems Design Symposium (CACSD) and International Symposium on Intelligent Control (ISIC) ; Conference date: 01-07-2006",

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TY - CONF

T1 - Radial basis function based iterative learning control for stochastic distribution systems

AU - Wang, Hong

AU - Afshar, Puya

PY - 2006

Y1 - 2006

N2 - In this paper, an Iterative Learning Control (ILC) scheme is presented for the control of the shape of the output probability density functions (PDF) for a class of stochastic systems in which the relationship between approximation basis functions and the control input is linear, and the stochastic system is not necessarily Gaussian. A Radial Basis Function Neural Network (RBFNN) has been employed for the output PDF approximation and the coefficients of the approximation are linearly related to the control input. A three-stage method for the ILC-based PDF control is proposed which incorporates a) identifying PDF model parameters; b) calculating the control input; and c) updating RFBN parameters. The latter is accomplished based on P-type ILC law and the difference of the desired and calculated output PDF within a batch. Conditions for the convergent ILC rules have been derived. Simulation results are included to demonstrate the effectiveness of proposed method. © 2006 IEEE.

AB - In this paper, an Iterative Learning Control (ILC) scheme is presented for the control of the shape of the output probability density functions (PDF) for a class of stochastic systems in which the relationship between approximation basis functions and the control input is linear, and the stochastic system is not necessarily Gaussian. A Radial Basis Function Neural Network (RBFNN) has been employed for the output PDF approximation and the coefficients of the approximation are linearly related to the control input. A three-stage method for the ILC-based PDF control is proposed which incorporates a) identifying PDF model parameters; b) calculating the control input; and c) updating RFBN parameters. The latter is accomplished based on P-type ILC law and the difference of the desired and calculated output PDF within a batch. Conditions for the convergent ILC rules have been derived. Simulation results are included to demonstrate the effectiveness of proposed method. © 2006 IEEE.

KW - Iterative learning mechanism

KW - Probability density functions

KW - RBF neural networks

KW - Stochastic systems

U2 - 10.1109/ISIC.2006.285585

DO - 10.1109/ISIC.2006.285585

M3 - Other

SP - 100

EP - 105

T2 - Joint 2006 IEEE Conference on Control Applications (CCA), Computer-Aided Control Systems Design Symposium (CACSD) and International Symposium on Intelligent Control (ISIC)

Y2 - 1 July 2006

ER -

Radial basis function based iterative learning control for stochastic distribution systems

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