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Inicio Revista Iberoamericana de Automática e Informática Industrial RIAI Identificación de sistemas en lazo cerrado basada en una estrategia híbrida AG...
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Vol. 10. Núm. 1.
Páginas 37-49 (enero - marzo 2013)
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Vol. 10. Núm. 1.
Páginas 37-49 (enero - marzo 2013)
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Open Access
Identificación de sistemas en lazo cerrado basada en una estrategia híbrida AGA-Simplex
Close-loop system identification based on an AGA-Simplex hybrid strategy
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5681
R.F. Tanda
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tanda@icimaf.cu

Autor para correspondencia.
, A. Aguado
Departamento de Control Automático, Instituto de Cibernética, Matemática y Física (ICIMAF), C.P. 10400, La Habana, Cuba
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La identificación de sistemas continuos en lazo cerrado, que puede ser enfocada como un problema de optimización no lineal, puede resultar de difícil solución mediante métodos convencionales. En este artículo se presenta una estrategia híbrida basada en un Algoritmo Genético Adaptable y el método Simplex, que resulta en una solución satisfactoria para dicho problema. Se compara la propuesta con otras técnicas reportadas en la literatura. Tres ejemplos exponen el desempeño del método: identificación de una dinámica de orden elevado; identificación de una dinámica de segundo orden inestable en lazo abierto; y estimación de parámetros en sistemas de generación eléctrica. Los resultados de simulación muestran que la propuesta es un método robusto para la identificación de sistemas en lazo cerrado.

Palabras clave:
Algoritmos de optimización
Algoritmos Genéticos
Estimación de parámetros
Identificación en lazo cerrado
Abstract

Closed-loop identification of continuous systems, which can be considered as a nonlinear optimization problem, may result in a difficult solution problem when conventional methods are used. In this paper it is presented a hybrid strategy based on an Adaptive Genetic Algorithm and the Simplex method, that results in a satisfactory solution for this problem. The proposal is compared with other techniques reported in the literature. Three examples show the performance of the method: identification of high order dynamics; identification of unstable second order dynamics in open-loop; and parameter estimation in power generation systems. Simulation results show that the proposed is a robust method for close-loop system identification.

Keywords:
Optimization algorithms
Genetic Algorithms
Parameter estimation
Closed-loop identification
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