TY - JOUR
T1 - Multiple Fuzzy Controls
AU - Watanabe, Keigo
AU - Shiramizu, Kozo
AU - Fukuda, Toshio
PY - 1995
Y1 - 1995
N2 - A multiple fuzzy controller is proposed for self-organizing fuzzy control in which several “elemental fuzzy controllers” are processed in parallel and the degree of usage of each inferred consequent is determined by using a linear neural network. The inputs to the neural network are all the inferred consequents generated from the elemental fuzzy controllers, and the output of the neural network is the control input to the plant. The delta rule is used to update the connection weights for the network so that the square of plant output deviation is minimized. The present approach allows the elemental fuzzy controller to be used in situations in which the controller parameters are tuned incompletely; thus the number of trials and errors required for tuning the parameters can be decreased significantly. The effectiveness of the present fuzzy controller is illustrated by a simulation for the attitude control of a flexible satellite.
AB - A multiple fuzzy controller is proposed for self-organizing fuzzy control in which several “elemental fuzzy controllers” are processed in parallel and the degree of usage of each inferred consequent is determined by using a linear neural network. The inputs to the neural network are all the inferred consequents generated from the elemental fuzzy controllers, and the output of the neural network is the control input to the plant. The delta rule is used to update the connection weights for the network so that the square of plant output deviation is minimized. The present approach allows the elemental fuzzy controller to be used in situations in which the controller parameters are tuned incompletely; thus the number of trials and errors required for tuning the parameters can be decreased significantly. The effectiveness of the present fuzzy controller is illustrated by a simulation for the attitude control of a flexible satellite.
KW - Automatic Control
KW - Computer Control
KW - Fuzzy Control
KW - Identification
KW - Iterative Learning Control
KW - Multiple Controllers
KW - Neural Networks
KW - Self-organizing
UR - http://www.scopus.com/inward/record.url?scp=0029328166&partnerID=8YFLogxK
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U2 - 10.1299/jsmec1993.38.227
DO - 10.1299/jsmec1993.38.227
M3 - Article
AN - SCOPUS:0029328166
SN - 1340-8062
VL - 38
SP - 227
EP - 232
JO - jsme international journal. ser. c, dynamics, control, robotics, design and manufacturing
JF - jsme international journal. ser. c, dynamics, control, robotics, design and manufacturing
IS - 2
ER -