Publication detail

Sliding Window Recursive Neural Networks Learning Algorithm and its Application on the Identification in Adaptive PID

PIVOŇKA, P. DOKOUPIL, J.

Original Title

Sliding Window Recursive Neural Networks Learning Algorithm and its Application on the Identification in Adaptive PID

Type

conference paper

Language

English

Original Abstract

This article deals with the implementation of the adaptive PID controller based on the principle of forced separation imposed on the identification and system control. Original implementation of both Gauss-Newton (GN) and Levenberg-Marquardt (LM) algorithms operating in recursive learning mode over exponential-sliding finite data window for modelling of nonlinear dynamic systems is suggested. Their dynamics can be represented by a feed forward neural network. Synthesis of the PID controller is achieved using the Ziegler-Nichols method which utilizes the linearized ARX model of the neural network at the working point of the process. Benefits of the suggested algorithms are illustrated in the example simulations on the mathematical model.

Keywords

Levenberg-Marquardt, Gauss-Newton, sliding-exponential window recursive algorithms, neural networks, NARX, adaptive PID controller

Authors

PIVOŇKA, P.; DOKOUPIL, J.

RIV year

2010

Released

15. 9. 2010

Location

Theodor-Korner-Allee 16 D-02763 Zittau

ISBN

978-3-9812655-4-5

Book

17th Zittau East-West Fuzzy Colloquium

Edition number

1

Pages from

55

Pages to

62

Pages count

8

BibTex

@inproceedings{BUT34626,
  author="Petr {Pivoňka} and Jakub {Dokoupil}",
  title="Sliding Window Recursive Neural Networks Learning Algorithm and its Application on the Identification in Adaptive PID",
  booktitle="17th Zittau East-West Fuzzy Colloquium",
  year="2010",
  number="1",
  pages="55--62",
  address="Theodor-Korner-Allee 16
D-02763 Zittau",
  isbn="978-3-9812655-4-5"
}