Robust Adaptive Observer Design for Uncertain Systems With Bounded Disturbances

This paper presents a robust adaptive observer design methodology for a class of uncertain nonlinear systems in the presence of time-varying unknown parameters with absolutely integrable derivatives, and nonvanishing disturbances. Using the universal approximation property of radial basis function (...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems 2007-09, Vol.18 (5), p.1392-1403
Main Authors: Stepanyan, V., Hovakimyan, N.
Format: Article
Language:English
Subjects:
Adaptive bounding
Adaptive systems
Algorithms
Applied sciences
Artificial intelligence
asymptotic observers
Asymptotic properties
Computer science
control theory
systems
Computer Simulation
Connectionism. Neural networks
Convergence
Design engineering
Design methodology
Disturbances
Errors
Exact sciences and technology
Feedback
Models, Theoretical
Neural networks
Neural Networks (Computer)
Nonlinear systems
Observers
Pattern Recognition, Automated - methods
radial basis functions (RBFs) approximation
Reproducibility of Results
Robustness
Sensitivity and Specificity
State estimation
Time varying systems
Uncertain systems
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Summary:This paper presents a robust adaptive observer design methodology for a class of uncertain nonlinear systems in the presence of time-varying unknown parameters with absolutely integrable derivatives, and nonvanishing disturbances. Using the universal approximation property of radial basis function (RBF) neural networks and the adaptive bounding technique, the developed observer achieves asymptotic convergence of state estimation error to zero, while ensuring boundedness of parameter errors. A comparative simulation study is presented by the end.
ISSN:1045-9227
2162-237X
1941-0093
2162-2388
DOI:10.1109/TNN.2007.895837