Brief paper: Robust adaptive observer for nonlinear systems with unmodeled dynamics
Automatica (Journal of IFAC)
Wavelet differential neural network observer
IEEE Transactions on Neural Networks
ACC'09 Proceedings of the 2009 conference on American Control Conference
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Real-time implementation of Chebyshev neural network observer for twin rotor control system
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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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.