Paper Info
Title
Adaptive Control System Based on Self-Organizing Wavelet Neural Network with H8 Tracking Performance Compensator
Abstract
Wavelet neural network (WNN) has high function approximation capability, because it consists of neurons, each of which has a localized and vibratory waveform, and the center of the waveform and its scaling and spatial extent/reduction are adjustable. Therefore it has outstanding ability to adapt to changes of environments. In the field of control engineering, Neural Network (NN) and Fuzzy Neural Network (FNN) are often used as a tool of nonlinear control system design. However it is seldom seen that WNN is used for control system designs. There may be one of only few cases that WNN is used as controller whose structure is furthermore fixed and it requires off-line learning to design the control system. In such case, it is difficult to react to change in the environment. So, we propose an adaptive wavelet neural network control system based on WNN with an adaptable self-organizing network structure and with H8 tracking performance compensator to be robust. In addition, we prove stability of the proposed system by Lyapunov stability analysis. Finally, through inverted pendulum control simulations, we showed the proposed system is superior to other conventional control systems.
Year
DOI
Venue
2013
10.1109/SMC.2013.551
SMC
Keywords
Field
DocType
H∞ control,Lyapunov methods,adaptive control,control engineering computing,control system synthesis,fuzzy neural nets,neurocontrollers,nonlinear control systems,pendulums,self-adjusting systems,waveform analysis,wavelet neural nets,FNN,H∞ tracking performance compensator,Lyapunov stability analysis,WNN,adaptive control system,control engineering,fuzzy neural network,high function approximation capability,inverted pendulum control,localized waveform,nonlinear control system design,self-organizing network,self-organizing wavelet neural network,vibratory waveform,Hinfinity tracking performance compensator,adaptive control,self-organizing,wavelet neural network
Control theory,Function approximation,Computer science,Nonlinear control,Control theory,Systems design,Lyapunov stability,Artificial intelligence,Adaptive control,Control system,Artificial neural network,Machine learning
Conference
ISSN
Citations 
PageRank 
1062-922X
0
0.34
References 
Authors
6
7
Name
Order
Citations
PageRank
Masanao Obayashi119826.10
Takuya Kamikariya200.34
Shogo Uchiyama300.34
Shogo Watada400.68
Takashi Kuremoto519627.73
Shingo Mabu649377.00
Kunikazu Kobayashi717321.96