TY - JOUR
T1 - Adaptive control of Wiener-type nonlinear systems using neural networks
AU - Yamanaka, Osamu
AU - Yoshizawa, Naoto
AU - Ohmori, Hiromitsu
AU - Sano, Akira
PY - 1998
Y1 - 1998
N2 - This paper proposes new adaptive control schemes with neural networks for Weiner-type nonlinear systems which have output nonlinearity. First, by adopting a robust adaptive control law and a functional link network (FLN), we present an adaptive linearizing scheme as a primary step for a model reference control scheme, where the FLN compensates the output nonlinearity. Second, we analyze the stability of the adaptive linearizing scheme by using a robust adaptive control technique, and demonstrate that all of the parameters are bounded and that the boundedness of all of the signals in the closed loop is guaranteed under some reasonable conditions. Third, based on the linearizing scheme, we present a new direct model reference adaptive control scheme by choosing the reference output appropriately. The stability of the system is guaranteed under several conditions in a similar manner. Finally, we illustrate the effectiveness of the proposed scheme through some numerical examples.
AB - This paper proposes new adaptive control schemes with neural networks for Weiner-type nonlinear systems which have output nonlinearity. First, by adopting a robust adaptive control law and a functional link network (FLN), we present an adaptive linearizing scheme as a primary step for a model reference control scheme, where the FLN compensates the output nonlinearity. Second, we analyze the stability of the adaptive linearizing scheme by using a robust adaptive control technique, and demonstrate that all of the parameters are bounded and that the boundedness of all of the signals in the closed loop is guaranteed under some reasonable conditions. Third, based on the linearizing scheme, we present a new direct model reference adaptive control scheme by choosing the reference output appropriately. The stability of the system is guaranteed under several conditions in a similar manner. Finally, we illustrate the effectiveness of the proposed scheme through some numerical examples.
KW - Adaptive control
KW - Neural network
KW - Nonlinear control
KW - Wiener-type nonlinear system
UR - http://www.scopus.com/inward/record.url?scp=0031654164&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0031654164&partnerID=8YFLogxK
U2 - 10.1002/(SICI)1520-6416(19980115)122:1<37::AID-EEJ5>3.0.CO;2-T
DO - 10.1002/(SICI)1520-6416(19980115)122:1<37::AID-EEJ5>3.0.CO;2-T
M3 - Article
AN - SCOPUS:0031654164
SN - 0424-7760
VL - 122
SP - 37
EP - 48
JO - Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)
JF - Electrical Engineering in Japan (English translation of Denki Gakkai Ronbunshi)
IS - 1
ER -