TY - JOUR
T1 - Fuzzy neural network control for vibration waveform system of mold
AU - Gao, Pu
AU - Li, Yunhua
AU - Sheng, Wanxing
PY - 2004/9
Y1 - 2004/9
N2 - Combining with the characteristic of the fuzzy control and the neural network control (NNC), a kind of the fuzzy neural network controller is proposed, and the synthesis design method of the control law and fast speed learning algorithm of the parameters of networks are put forward. The output of the controller is composed of two parts: Part one is derived on basis of the principle of sliding control, the lower order model and the estimated parameters of the plant are only required; Part two is derived on basis of FNN, which is used to compensate the uncertainties of the systems. Because the type of FNN controller extracts from the advantages of the intelligent control and model based sliding mode control, the number of the adjusting parameters and the structure of FNN are simplified at large, and the practical significance and variation range are attached to each layer of the network and its connected weights, the control performance and learning speed are increased at large. The Tightness of the conclusions is verified by the experiment of an electro-hydraulic position servo system of the mold of the continuous casting machinery.
AB - Combining with the characteristic of the fuzzy control and the neural network control (NNC), a kind of the fuzzy neural network controller is proposed, and the synthesis design method of the control law and fast speed learning algorithm of the parameters of networks are put forward. The output of the controller is composed of two parts: Part one is derived on basis of the principle of sliding control, the lower order model and the estimated parameters of the plant are only required; Part two is derived on basis of FNN, which is used to compensate the uncertainties of the systems. Because the type of FNN controller extracts from the advantages of the intelligent control and model based sliding mode control, the number of the adjusting parameters and the structure of FNN are simplified at large, and the practical significance and variation range are attached to each layer of the network and its connected weights, the control performance and learning speed are increased at large. The Tightness of the conclusions is verified by the experiment of an electro-hydraulic position servo system of the mold of the continuous casting machinery.
KW - Electro-hydraulic servo system
KW - Fuzzy control
KW - Neural networks
KW - Sliding mode control
UR - https://www.scopus.com/pages/publications/9444279578
U2 - 10.3901/cjme.2004.03.472
DO - 10.3901/cjme.2004.03.472
M3 - 文章
AN - SCOPUS:9444279578
SN - 1000-9345
VL - 17
SP - 472
EP - 476
JO - Chinese Journal of Mechanical Engineering (English Edition)
JF - Chinese Journal of Mechanical Engineering (English Edition)
IS - 3
ER -