Artificial neural network for piezoelectric control systems

J. Bakhashwain; J. Refaee; M. Sunar; M. Mohandes

doi:10.1117/12.350118

Artificial neural network for piezoelectric control systems

J. Bakhashwain^*, J. Refaee, M. Sunar, M. Mohandes

^*Corresponding author for this work

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

This paper presents a neural network controller for a piezoelectric controlled structure by emulating the control performance of a Linear Quadratic Gaussian (LQG) controller. The configuration of the Artificial Neural Network (ANN) is simple, yet it is efficient in terms of its high learning speed and good generalization ability. A case study is presented to demonstrate the performance of the ANN controller versus the LQG controller. The test results for different disturbances on the structure show excellent agreement between the ANN and LQG controllers.

Original language	English
Pages (from-to)	691-699
Number of pages	9
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	3667
DOIs	https://doi.org/10.1117/12.350118
State	Published - 1999

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

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title = "Artificial neural network for piezoelectric control systems",

abstract = "This paper presents a neural network controller for a piezoelectric controlled structure by emulating the control performance of a Linear Quadratic Gaussian (LQG) controller. The configuration of the Artificial Neural Network (ANN) is simple, yet it is efficient in terms of its high learning speed and good generalization ability. A case study is presented to demonstrate the performance of the ANN controller versus the LQG controller. The test results for different disturbances on the structure show excellent agreement between the ANN and LQG controllers.",

author = "J. Bakhashwain and J. Refaee and M. Sunar and M. Mohandes",

year = "1999",

doi = "10.1117/12.350118",

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TY - JOUR

T1 - Artificial neural network for piezoelectric control systems

AU - Bakhashwain, J.

AU - Refaee, J.

AU - Sunar, M.

AU - Mohandes, M.

PY - 1999

Y1 - 1999

N2 - This paper presents a neural network controller for a piezoelectric controlled structure by emulating the control performance of a Linear Quadratic Gaussian (LQG) controller. The configuration of the Artificial Neural Network (ANN) is simple, yet it is efficient in terms of its high learning speed and good generalization ability. A case study is presented to demonstrate the performance of the ANN controller versus the LQG controller. The test results for different disturbances on the structure show excellent agreement between the ANN and LQG controllers.

AB - This paper presents a neural network controller for a piezoelectric controlled structure by emulating the control performance of a Linear Quadratic Gaussian (LQG) controller. The configuration of the Artificial Neural Network (ANN) is simple, yet it is efficient in terms of its high learning speed and good generalization ability. A case study is presented to demonstrate the performance of the ANN controller versus the LQG controller. The test results for different disturbances on the structure show excellent agreement between the ANN and LQG controllers.

UR - https://www.scopus.com/pages/publications/0032662463

U2 - 10.1117/12.350118

DO - 10.1117/12.350118

M3 - Conference article

AN - SCOPUS:0032662463

SN - 0277-786X

VL - 3667

SP - 691

EP - 699

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

ER -

Artificial neural network for piezoelectric control systems

Abstract

ASJC Scopus subject areas

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