Technical communique: Further result on a dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems

Authors:
S. N. Huang;K. K. Tan;T. H. Lee
Affiliations:
Department of Electrical and Computer Engineering, National University of Singapore, Singapore 119260, Singapore;Department of Electrical and Computer Engineering, National University of Singapore, Singapore 119260, Singapore;Department of Electrical and Computer Engineering, National University of Singapore, Singapore 119260, Singapore
Venue:
Automatica (Journal of IFAC)
Year:
2005

Citing 1
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A dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems

Automatica (Journal of IFAC)

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Abstract

In Kim et al. [(1997) A dynamic recurrent neural-network-based adaptive observer for a class of nonlinear systems. Automatica 33(8), 1539-1543], authors present an excellent neural network (NN) observer for a class of nonlinear systems. However, the output error equation in their paper is strictly positive real (SPR) which is restrictive assumption for nonlinear systems. In this note, by introducing a vector b"0 and Lyapunov equation, the observer design is obtained without requiring the SPR condition. Thus, our observer can be applied to a wider class of systems.