TY - GEN

T1 - Neural network approaches to some model flow control problems

AU - Cho, Yongseung

AU - Agarwal, Ramesh K.

AU - Nho, Kyungmoon

N1 - Publisher Copyright:
© 1997 by Yongseung Cho, Ramesh K. Agarwal, and Kyungmoon Nho. Published by the American Institute of Aeronautics and Astronautics, Inc.

PY - 1997

Y1 - 1997

N2 - The application of two neural network approaches (supervised neural network and adaptive neural network) to control some flows modeled by the Burgers equation and the Wave equation is studied. Due to its capability of handling nonlinearity and its parallel processing structure, a neural network is suitable as an adaptive controller in real time. The simple mapping type feedforward multilayer neural network controller is trained with the data set obtained from the optimal and robust control laws, while the adaptive neural network controller is obtained by applying a feedback linearization method. The neural network is used to estimate the function of the system. The simple mapping type neural network requires off-line (or supervised) training with the sets of data while the adaptive neural network controller does not require an off-line training phase. The dynamic sequential recursive backpropagation algorithm is used to train the adaptive neural network on-line according to a modeling error. Both approaches result in successfully obtaining the controller for randomly disturbed flows. It should be noted, however, that the simple mapping type neural network controller results in offset from zero, even though the offset is very small and is adjusted easily.

AB - The application of two neural network approaches (supervised neural network and adaptive neural network) to control some flows modeled by the Burgers equation and the Wave equation is studied. Due to its capability of handling nonlinearity and its parallel processing structure, a neural network is suitable as an adaptive controller in real time. The simple mapping type feedforward multilayer neural network controller is trained with the data set obtained from the optimal and robust control laws, while the adaptive neural network controller is obtained by applying a feedback linearization method. The neural network is used to estimate the function of the system. The simple mapping type neural network requires off-line (or supervised) training with the sets of data while the adaptive neural network controller does not require an off-line training phase. The dynamic sequential recursive backpropagation algorithm is used to train the adaptive neural network on-line according to a modeling error. Both approaches result in successfully obtaining the controller for randomly disturbed flows. It should be noted, however, that the simple mapping type neural network controller results in offset from zero, even though the offset is very small and is adjusted easily.

UR - http://www.scopus.com/inward/record.url?scp=84983135073&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84983135073

SN - 9780000000002

T3 - 4th Shear Flow Control Conference

BT - 4th Shear Flow Control Conference

PB - American Institute of Aeronautics and Astronautics Inc, AIAA

T2 - 4th AIAA Shear Flow Control Conference, 1997

Y2 - 29 June 1997 through 2 July 1997

ER -