Algoritmo genético aplicado a la sintonización de un controlador PID  para un sistema acoplado de tanques

Arellano, Othon Colorado; Romero, Norberto Hernández; Mora, Juan; Marín, Joselito Medina

doi:10.29057/icbi.v5i10.2935

Cited by 3 publications

(2 citation statements)

References 6 publications

(8 reference statements)

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“…Then, greater probability of crossing is given to the best ranked, forming an arrangement referred to as "strand", where the numbers that identify the chromosomes are repeated proportionally to their importance . For example, assuming that the first four chromosomes in the population have been chosen (1,2,3,4), they generate a "strand", giving the first chromosome higher priority because it is the best ranked within the population. It is constructed as follows:…”

Section: Selectionmentioning

confidence: 99%

PID Controller with Real Number Genetic Algorithms

Muñoz¹

2020

idata

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Un algoritmo genético o genetic algorithm (GA) es una técnica de la inteligencia artificial que es utilizada en cualquiera de las especialidades de ingeniería. En el presente estudio, el GA propuesto encuentra valores óptimos para los parámetros Kp, Ki y Kd de un controlador PID, utilizado ampliamente en la industria. Los cromosomas, formados con los genes Kp, Ki y Kd, representados por números reales, evolucionan y son evaluados mediante el error cuadrático medio (ECM) de la salida deseada. En ese sentido, la solución es el cromosoma con menor ECM, el cual produce menores transitorios en la salida. Además, el GA ha sido codificado en lenguaje M (MATLAB) y los resultados han sido comparados con otros trabajos.

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Section: Selectionmentioning

confidence: 99%

PID Controller with Real Number Genetic Algorithms

Muñoz¹

2020

idata

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“…El algoritmo genético continuo es más rápido que el binario, debido a que los cromosomas no tienen que ser decodificados antes de la evaluación de la función de costo (Colorado-Arellano O. et al, 2018).…”

Section: Sintonización De Las Ganancias De Los Controladores Medianteunclassified

Modelado y Análisis de Sintonización de Velocidad de un MSIP con Presencia de Fisura Mediante Algoritmos Genéticos

Garcia

Cambero

Anieva

2019

Rev. iberoam. autom. inform. ind.

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ResumenEn el presente artículo se propone el modelado del sistema continuo correspondiente al motor síncrono de imanes permanentes (MSIP) con presencia de degradación en la inercia rotacional del rotor, mediante el mecanismo de fisura, lo cual permite la estimación de parámetros físicos, en el caso de análisis, el diámetro de la grieta presente en el rotor, disminuyendo con ello la incertidumbre en el modelado del MSIP. Se lleva a cabo la identificación en bucle cerrado de las ganancias mediante un análisis de sensibilidad, donde mediante el método computacional de optimización de búsqueda por algoritmos genéticos se obtienen las ganancias que logran converger el modelo propuesto al modelo de referencia. Palabras Clave:Modelado del sistema continuo, Estimación de parámetro, Incertidumbre en el modelado, Identificación en bucle cerrado, Método computacional de optimización, Degradación en Inercia, Mecanismo de Fisura, Sensibilidad, Algoritmo genético. Modeling and Analysis of speed tuning of a PMSM with presence of crack using genetic algorithms AbstractIn this article, we propose the continuous system model corresponding to the Permanent Magnet Synchronous Motor (PMSM) with the presence of degradation in the rotational inertia of the rotor through the fissure mechanism, which allows the estimation of the physical parameters. We consider the diameter of the split on the rotor in the analysis of the model, and in effect, the decrease of the uncertainty of the PMSM model. The closed-loop identification of the gains is achieved by a sensibility analysis, whilst the gains obtained by the computational optimization method of search through genetic algorithms, allows the proposed model converge to the reference model.

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Trajectory Simulation Approach for Autonomous Vehicles Path Planning using Deep Reinforcement Learning

Lima

Oliveira

Costa

2020

Int J Innov Educ Res

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Autonomous vehicle path planning aims to allow safe and rapid movement in an environment without human interference. Recently, Reinforcement Learning methods have been used to solve this problem and have achieved satisfactory results. This work presents the use of Deep Reinforcement Learning for the task of path planning for autonomous vehicles through trajectory simulation, to define routes that offer greater safety (without collisions) and less distance for the displacement between two points. A method for creating simulation environments was developed to analyze the performance of the proposed models in different difficult degrees of circumstances. The decision-making strategy implemented was based on the use of Artificial Neural Networks of the Multilayer Perceptron type with parameters and hyperparameters determined from a grid search. The models were evaluated for their reward charts resulting from their learning process. Such evaluation occurred in two phases: isolated evaluation, in which the models were inserted into the environment without prior knowledge; and incremental evaluation, in which models were inserted in unknown environments with previous intelligence accumulated in other conditions. The results obtained are competitive with state-of-the-art works and highlight the adaptive characteristic of the models presented, which, when inserted with prior knowledge in environments, can reduce the convergence time by up to 89.47% when compared to related works.

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Algoritmo genético aplicado a la sintonización de un controlador PID para un sistema acoplado de tanques

Cited by 3 publications

References 6 publications

PID Controller with Real Number Genetic Algorithms

PID Controller with Real Number Genetic Algorithms

Modelado y Análisis de Sintonización de Velocidad de un MSIP con Presencia de Fisura Mediante Algoritmos Genéticos

Trajectory Simulation Approach for Autonomous Vehicles Path Planning using Deep Reinforcement Learning

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