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This paper presents a neurodynamic optimization approach to robust pole assignment for synthesizing linear control systems via state and output feedback. The problem is formulated as a pseudoconvex optimization problem with robustness measure: i.e., the spectral condition number as the objective function and linear matrix equality constraints for exact pole(More)
In this paper, a neurodynamic optimization approach is proposed for synthesizing high-order descriptor linear systems with state feedback control via robust pole assignment. With a new robustness measure serving as the objective function, the robust eigenstructure assignment problem is formulated as a pseudoconvex optimization problem. A neurodynamic(More)
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(More)
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(More)
Dynamins are large superfamily GTPase proteins that are involved in various cellular processes including budding of transport vesicles, division of organelles, cytokinesis, and pathogen resistance. Here, we characterized several dynamin-related proteins from the rice blast fungus Magnaporthe oryzae and found that MoDnm1 is required for normal functions,(More)
This paper presents a neurodynamic optimization approach to robust pole assignment for synthesis of piecewise linear control systems via state feedback. The robust pole assignment is formulated as a pseudoconvex optimization problem with linear equality constraints where a robustness measure is considered as the objective function. The robustness is(More)
This paper presents an application of vibration control to a half-car model using recurrent neural networks. The robust vibration control is formulated as equality constrained optimization problem. Simulation results show that the close-loop system has good response performance in the presence of disturbances generated by an isolated bump. The study shows(More)
A collective neurodynamic system is presented to distributed convex optimization subject to linear equality and box constraints in framework of an autonomous multiagent network. The overall objective to be minimized takes an additive form of multiple local objective functions. Agents in the system, each of which is modeled by a recurrent neural network,(More)
This paper presents a neurodynamic optimization approach with two coupled recurrent neural networks for the synthesis of linear systems with fault detection via robust pole assignment. The proposed approach is shown to be capable of synthesizing control systems with robust state estimators and fault detection with parameter perturbation. The operating(More)