A neurodynamic optimization approach to robust pole assignment for synthesizing linear state feedback control systems

This paper presents a neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems via state feedback. A pseudoconvex objective function is minimized as a robustness measure. A neurodynamic model is applied whose global convergence was theoretically proved for constrained pseudoconvex optimization. Compared with existing approaches on benchmark problems, the convergence of proposed neurodynamic approach to global optimal solutions can be guaranteed. Simulation results of the proposed neurodynamic approach is reported to demonstrate its superiority.

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