Paper Info
Title
Robust Adaptive Control Via Neural Linearization And Four Types Of Compensation
Abstract
In this paper, we propose a new type of neural adaptive control via dynamic neural networks. For a class of unknown nonlinear systems, a neural identifier-based feedback linearization controller is first used. Dead-zone and projection techniques are applied to assure the stability of neural identification. Then four types of compensator are addressed. The stability of closed-loop system is also proven.
Year
DOI
Venue
2008
10.1109/IJCNN.2008.4634043
2008 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-8
Keywords
Field
DocType
nonlinear system,adaptive control,stability,robustness,feedback,control systems,neurofeedback,feedback linearization,stability analysis,robust control,neural networks,nonlinear systems,artificial neural networks
Control theory,Control theory,Computer science,Feedback linearization,Robustness (computer science),Artificial intelligence,Adaptive control,Control system,Artificial neural network,Robust control,Machine learning,Linearization
Conference
ISSN
Citations 
PageRank 
2161-4393
0
0.34
References 
Authors
7
2
Name
Order
Citations
PageRank
Wen Yu128322.70
Xiaoou Li255061.95