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

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Original languageEnglish
Pages (from-to)923-928
Journal / PublicationJournal of Guidance, Control, and Dynamics
Issue number6
Publication statusPublished - Nov 1998


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.