Robust approximate pole assignment for second-order systems: Neural network computation

Daniel W. C. Ho, James Lam, Jinhua Xu

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

19 Citations (Scopus)

Abstract

A recurrent neural network approach to robust approximate pole assignment for second-order systems is proposed. The design is formulated as an unconstrained optimization problem and solved via the gradient-flow approach, which is ideally suited for neural network implementation. Convergence of the gradient flow also is established. Simulation results are used to demonstrate the effectiveness of the proposed method.
Original languageEnglish
Pages (from-to)923-928
JournalJournal of Guidance, Control, and Dynamics
Volume21
Issue number6
DOIs
Publication statusPublished - Nov 1998

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