Evolutionary algorithms based parameters tuning of PID controller

Li, Junli; Mao, Jianlin; Zhang, Guanghui

doi:10.1109/ccdc.2011.5968215

Cited by 10 publications

(6 citation statements)

References 4 publications

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“…In addition, these articles tackled the tuning gains of both PID and FOPID controllers with various optimization techniques in different domains and for different applications. Table .1 represents a comparison among these papers [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30].…”

Section: Related Workmentioning

confidence: 99%

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Review of Various Metaheuristics Techniques for Tuning Parameters of PID/FOPID Controllers

2022

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Alongside the rapid advancement of technology, researchers are looking for accurate, flexible, and efficient ways to control systems. One big breakthrough in technology is the PID and its following advancement which is FOPID controller. The PID controller is becoming widely used due to its simplicity and cost-effectiveness, while the latter is more desirable because of its accuracy and flexibility. The parameters of the aforementioned controllers have an enormous effect on the transient responses. For instance, the settling time, rising time, Integral Absolute Error (IAE), and overshooting are greatly influenced by the controller parameters. However, the most difficult problem in delivering the active case of the transient response of any system is the tuning of system parameters. Ziegler-Nicolas (ZN), recursive least squares (RLS) for tuning PID controller, while Pole Placement, and Pole Distribution for tuning FOPID are some examples of classical algorithms that are utilized previously to tackle this problem. Recently, the optimization methods have been employed to tune these parameters in order to have a more stable controller system. This systematic literature review (SLR) will take into consideration various optimization methods that tackled this problem. Methods like PSO, GA and ACO are the most utilized in tackling this problem. This paper will be an introductory guide to other researchers which will immensely help in their metaheuristic method selection.

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Section: Related Workmentioning

confidence: 99%

“…From the pool value is came after the first step. The probability computation that is represented in equation (16).…”

Section: Ant Colony Optimization (Aco)mentioning

confidence: 99%

Review of Various Metaheuristics Techniques for Tuning Parameters of PID/FOPID Controllers

2022

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“…El uso de técnicas evolutivas y en específico de algoritmos genéticos en la sintonización de controladores PID es documentado en la literatura especializada, como muestra Mohd S et al [6] donde realizan una comparación entre evolución diferencial, algoritmos genéticos y criterio de Zigler-Nichols, demostrando que las técnicas evolutivas presentan ventajas en cuanto al desempeño con respecto a técnicas analíticas como Zigler-Nichols. Li J y otros [7] realizaron un algoritmo genético con codificación real, el cual no demuestra diferencias significativas con respecto al uso de un enjambre de partículas. Reynoso G et al [8] muestra un procedimiento de sintonización automática de parámetros de un controlador PID en forma serie, el cual muestra ventajas sobre Zigler-Nichols al controlar la respuesta de un conjunto de plantas de prueba propuestas de manera teórica.…”

Section: Introductionunclassified

“…Yang M et al controlan la velocidad de rotación de un motor a través de un algoritmo genético cuyo criterio de paro es la convergencia del algoritmo. En base a lo documentado en el estado del arte se puede aseverar que la sintonización de controladores tipo PID realizada por algoritmos genéticos ofrecen mayores ventajas que el criterio de Zigler-Nichols, [6,8,10], cabe destacar que la codificación empleada en los citados trabajos fue binara, esto puede presentar errores de truncamiento en el momento de representar las variables k p , k i , k d ; esto es solucionado por una codificación real en [7] y [9], por último en [11] se documenta el paro de algoritmo genéticos por medio de la convergencia de la función objetivo.…”

Section: Introductionunclassified

Sintonización de un controlador Proporcional-Integral Derivativo aplicado a una celda termoeléctrica: Una comparación entre algoritmos genéticos

Mejía

Sánchez²,

Fuentes³

et al. 2015

RCS

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Resumen. Las celdas termoeléctricas son dispositivos semiconductores que se usan en la refrigeración móvil, su comportamiento, es decir la forma que responden a ciertos estímulos eléctricos puede manipularse por medio de técnicas de ingeniería de control, la más popular, el controlador Proporcional-Integral-Derivativo cuyos parámetros son sintonizados por medio del criterio de Zigler-Nichols; el cual, en este artículo, es comparado con un algoritmo genético canónico de 40 cromosomas y con una versión reducida de este, denominado micro algoritmo genético de 5 individuos. En este trabajo ambas técnicas evolutivas son codificadas con números reales y recombinadas por medio de operadores de cruce aritmético no uniforme. Los resultados generados por los algoritmos propuestos son simulados mediante Scilab.

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“…Until the date, diverse tuning evolutionary algorithms have been implemented such as Genetic Algorithms (GAs) [11], [12] and Accumulated Genetic Algorithms (AGAs) [13] among others. The GAs take an initial population of possible solutions doing cross, mutation, evaluation and selection between these possible solutions, developing generations with better fitness than their predecessors, improving the performance of the system in every generation [14].…”

Section: Introductionmentioning

confidence: 99%

Tuning of a fuzzy PD type controller applied to the buck converter using the NSGA-II algorithm

Sanabria

Garzon

2012

2012 Workshop on Engineering Applications

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The buck converter is an electronic device widely used in power electric applications. This converter has a nonlinear performance in consequence of the switching transistor effects which it uses. Implement a classic controller for this converter results very complex. Hence, fuzzy controllers are used to obtain a better steady state performance of the system. This paper presents a multi-objective optimization to ensure a steady state error (e ss )<0.03 and a ripple factor (Fr)<0.03. We use the NSGA-II multi-objective algorithm, because this has an elitist approach that guarantees a great genetic diversity. The obtained results are validated using simulation of the algorithm obtaining a smaller e ss and Fr than the previous works. This work represents an innovative contribution in the control of buck converters, due to the fact that there´s no works in the literature that tunes this devices using NSGA-II.

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Evolutionary algorithms based parameters tuning of PID controller

Cited by 10 publications

References 4 publications

Review of Various Metaheuristics Techniques for Tuning Parameters of PID/FOPID Controllers

Review of Various Metaheuristics Techniques for Tuning Parameters of PID/FOPID Controllers

Sintonización de un controlador Proporcional-Integral Derivativo aplicado a una celda termoeléctrica: Una comparación entre algoritmos genéticos

Tuning of a fuzzy PD type controller applied to the buck converter using the NSGA-II algorithm

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