Development of neural fractional order PID controller with emulator.
ISA Trans
; 106: 293-302, 2020 Nov.
Article
en En
| MEDLINE
| ID: mdl-32616354
ABSTRACT
This paper focuses on tuning parameters of fractional order PID controller (FOPID) by using neural networks (NNs). For tuning the coefficients of the controller and orders of fractional derivative and integrator, five exclusive NNs are employed. Moreover, an emulator is used to identify the plant's behavior. Extended Kalman Filter (EKF) algorithm is used to update the weights of the controller's NNs, and Back Propagation (BP) algorithm is used for the weight updating procedure of the emulator's NNs. The proposed neural fractional order PID controller (NFOPID) is capable of being applied to various plants. Thus, two plants with different dynamics are examined. One is vibration damping of a Euler-Bernoulli beam, which has a fast dynamic, and the other is a time-delayed system like temperature control of a tempered glass furnace. The controller could deal appropriately with these tasks and is compared for accuracy and robustness with other controllers. The results were satisfactory for both systems.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Redes Neurales de la Computación
Idioma:
En
Revista:
ISA Trans
Año:
2020
Tipo del documento:
Article
País de afiliación:
Irán
Pais de publicación:
EEUU
/
ESTADOS UNIDOS
/
ESTADOS UNIDOS DA AMERICA
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EUA
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UNITED STATES
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UNITED STATES OF AMERICA
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US
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USA