A Genetic Algorithm Convergence and Models for Eigenstructure Assignment via Linear Quadratic Regulator (LQR)

Fonseca, João Viana da; Abreu, Ivanildo Silva; Rêgo, Patrícia Helena Moraes; Wolff, Maren; Silva, O.F.

doi:10.1109/tla.2008.4461626

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…El índice de control es un requisito entre la calidad del control y los costos de control (Teppa et al, 2015), (Viana et al, 2008). La calidad de control determina la primera parte de la expresión integrada.…”

Section: Modelo Y Controlunclassified

Modelado y control de temperatura para una clase de sistema derivado de la ley de enfriamiento de Newton

Ojeda-Misses

2022

ICBI

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En este trabajo se presenta el modelado y el control para una clase de sistema derivado de la ley de enfriamiento de Newton En este artículo un sistema térmico es representado mediante un modelo de parámetros concentrados, cuyas sustancias se caracterizan por una resistencia al flujo de calor tienen una capacitancia térmica, que por lo común tienen perdida de calor. Por lo tanto, se requiere implementar una clase de controlador que permita optimizar la función de costo y permita aprovechar el rendimiento con la menor perdida de energía. Para satisfacer lo anterior, se emplea un controlador proporcional derivativo (PD) bajo la sintonización de un regulador cuadrático lineal (LQR) que permite minimizar el criterio de desempeño. Los resultados son llevados a cabo mediante experimentos en tiempo real mediante el uso de Matlab, Arduino Uno y el termostato W1209. Finalmente, en el artículo son presentados los resultados experimentales y las conclusiones correspondientes, que permiten validar el desempeño del modelo propuesto y el controlador implementado.

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Section: Modelo Y Controlunclassified

Modelado y control de temperatura para una clase de sistema derivado de la ley de enfriamiento de Newton

Ojeda-Misses

2022

ICBI

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“…Genetic algorithms can be used to find an adequate selection of constraint matrices Q and R according to the design conditions described in a cost function or a performance index [8].…”

Section: B Controller Tuning By Genetic Algorithmsmentioning

confidence: 99%

Multivariable controller tuning using genetic algorithms for an induction motor

Albarracin-Avila

Giraldo

2014

2014 IEEE 5th Colombian Workshop on Circuits and Systems (CWCAS)

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A methodology for tuning multivariable controllers in state space by using genetic algorithms is proposed. The multivariable controller is designed in discrete time by using an extended state space feedback with integral action. The feedback gain is computed by using a linear quadratic regulator. Optimal constraints are tuned by genetic algorithms according to a cost function. As a result a robust multivariable controller is obtained by using state space feedback. This tuning procedure avoids the knowledge of the exact parameters of the system, and has the advantage that reduces the tracking error and decrease the rise time based on a performance index. Proposed methodology is applied over an induction motor in discrete time. The performance of the proposed methodology is compared against fixed controllers calculated by linear quadratic methods.

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“…The Genetic Algorithm which is oriented to Q and R matrices search was developed by [5] and details of its convergence performance evaluation are shown in [4]. The artificial neural network structure to solve ARE was developed by [13] and an evaluation of the parameter tuning sensitivity is reported in [2], where the ARE solution is evaluated in terms of stability and solvability for several dynamic systems.…”

Section: Introductionmentioning

confidence: 99%

A neural genetic training for LQR controllers tuning applied to inverted pendulum

Renan

Neto

Albuquerque

2009

2009 IEEE Student Conference on Research and Development (SCOReD)

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In this article is presented a method to design neural-genetic optimal controllers that are based on the fusion of a Recurrent Neural Network (RNN) and Genetic Algorithm (GA), these Computational Intelligence (CI) paradigms support the Linear Quadratic (LQR) design. The GA and RNN adaptation proprieties are the great advantage of the proposed approach, because all design is oriented to tune the optimal controller without inference of the human. A 4 th order model of an inverted pendulum is used to evaluate the training and control performance of the proposed method.

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A Genetic Algorithm Convergence and Models for Eigenstructure Assignment via Linear Quadratic Regulator (LQR)

Cited by 10 publications

References 12 publications

Modelado y control de temperatura para una clase de sistema derivado de la ley de enfriamiento de Newton

Modelado y control de temperatura para una clase de sistema derivado de la ley de enfriamiento de Newton

Multivariable controller tuning using genetic algorithms for an induction motor

A neural genetic training for LQR controllers tuning applied to inverted pendulum

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