Neurodynamic optimization approaches to robust pole assignment based on alternative robustness measures

This paper presents new results on neurodynamic optimization approaches to robust pole assignment based on four alternative robustness measures. One or two recurrent neural networks are utilized to optimize these measures while making exact pole assignment. Compared with existing approaches, the present neurodynamic approaches can result in optimal robustness in most cases with one of the robustness measures. Simulation results of the proposed approaches for many benchmark problems are reported to demonstrate their performances.

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