RBF Based Induction Motor Control with a Good Nonlinearity Compensation

Özçalık, Hasan Rıza; Yıldız, Ceyhun; Danacı, Mustafa; Koca, Zafer

doi:10.1007/978-3-540-73007-1_106

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…Los ejemplos más conocidos de las aplicaciones de la Inteligencia Artificial son aquellos basados en lógica difusa y redes neuronales artificiales. En los últimos años, se han desarrollado una gran cantidad de métodos de control y modelado basados en lógica difusa y redes neuronales artificiales y se han utilizado para controlar el péndulo invertido y otros sistemas complejos [4][5][6][7].…”

Section: Introductionunclassified

Convergencia de la trayectoria lingüística en el espacio de estados de un Controlador Difuso aplicado a un Sistema No Lineal

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Hernández-Luna

Luna-Carrasco

2019

JSL

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This article presents the design of a fuzzy controller to stability analysis base on the convergence of the linguistic trajectory in the state of space, for an inverted car-pendulum system, the fuzzy controller is of the Mamdani type and, it consists of 25 rules, 3 input variables and each one is composed of five memberships functions. The inverted car-pendulum system is represented by a non-linear model, which is obtained from a linearized equivalent model under the consideration of small oscillations. Results are validated against a PID control base on the trajectory on the phase plane to evaluate the efficiency and effectiveness of the fuzzy controller. The dynamic behavior of the system of both controllers is obtained with a unit impulse input, the simulation of the control system is developed on the MATLAB / SIMULINK software using the FUZZY LOGIC TOOLBOX, which allows to perform test and simulations, and also it shows results of graphic form.

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Section: Introductionunclassified

Convergencia de la trayectoria lingüística en el espacio de estados de un Controlador Difuso aplicado a un Sistema No Lineal

Téllez-Cuevas

Hernández-Luna

Luna-Carrasco

2019

JSL

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“…Ancak karmaşık ve doğrusal olmayan yapıları nedeniyle asenkron motorları geniş bir yelpazede kontrol etmek oldukça zor olmaktadır. Bu nedenle kontrol hedeflerine ulaşmak güçlü ve sağlam kontrol algoritmaları yanında gelişmiş motor sürücü sistemleri de gerektirmektedir [1][2][3][4][5]. motor kontrol uygulamalarında performans gereksiniminin vektör kontrol tekniğiyle karşılanabildiği görülmektedir.…”

Section: Introductionunclassified

An Efficient Adaptive Controller Design for Three Phase Induction Motors Based on RBF Neural Network

Kılıç¹,

Şit²,

Özçalık³

et al. 2017

EJT

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Adaptive controller with RBF neural network for induction motor drive

Kılıç

Özçalık

Şit

2017

Int J Numerical Modelling

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In this paper, the radial basis function neural network‐based model reference adaptive speed control for vector controlled induction motor drive system is presented. The speed control of induction motors is challenging because of their complex mathematical model, non‐linear structure, and time varying dynamics. The radial basis function neural network is used to compensate the non‐linearity which comes from the non‐linear state equations of induction motor model. Neural network parameters are online updated via gradient descent algorithm to minimize the error. The drive system has been tested under various operating conditions. This paper demonstrated benefits of the proposed control approach by comparing the algorithm to conventional PI controllers. The results show that the proposed controller ensures good robustness and stable operation of the system under variable speed and variable loads than the PI controller.

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RBF Based Induction Motor Control with a Good Nonlinearity Compensation

Cited by 7 publications

References 12 publications

Convergencia de la trayectoria lingüística en el espacio de estados de un Controlador Difuso aplicado a un Sistema No Lineal

Convergencia de la trayectoria lingüística en el espacio de estados de un Controlador Difuso aplicado a un Sistema No Lineal

An Efficient Adaptive Controller Design for Three Phase Induction Motors Based on RBF Neural Network

Adaptive controller with RBF neural network for induction motor drive

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