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Title: Hybridization of IMC and PID control structures based on filtered GPC using genetic algorithm

Abstract

Classical and advanced hybridization methods are employed in the control literature to calibrate the tuning parameters of internal model control (IMC) and proportional-integral-derivative (PID) controllers. This paper presents an alternative tuning design for the generalized predictive controller (GPC) that is based on both the positional process model and cost function. The proposed method involves the selection of an integral polynomial weighting filter for reference and output signals to deal with reference tracking, disturbance rejection, model-plant mismatch (MPM), minimum control energy and closed-loop robustness. The filter positional GPC (FP-GPC) obtained is transformed into a two-degree-of-freedom polynomial filtered RST structure and then into filtered internal model control (F-IMC) and PID controllers. Also, a multi-objective optimization based on genetic algorithm is applied to tune the filter parameters. Numerical and experimental essays show the effectiveness of the proposed control methodology.

Authors:
 [1];  [2]
  1. Federal Institute of Education Science and Technology of Pará, Department of Control and Automation Engineering (Brazil)
  2. Federal University of Santa Catarina, Department of Automation and Systems (Brazil)
Publication Date:
OSTI Identifier:
22769317
Resource Type:
Journal Article
Journal Name:
Computational and Applied Mathematics
Additional Journal Information:
Journal Volume: 37; Journal Issue: 2; Other Information: Copyright (c) 2018 SBMAC - Sociedade Brasileira de Matemática Aplicada e Computacional; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0101-8205
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICAL METHODS AND COMPUTING; DEGREES OF FREEDOM; DISTURBANCES; GENETIC ALGORITHMS; GENETICS; HYBRIDIZATION; OPTIMIZATION; POLYNOMIALS

Citation Formats

Araújo, Rejane De B., E-mail: rejane.barros@ifpa.edu.br, and Coelho, Antonio A. R.,. Hybridization of IMC and PID control structures based on filtered GPC using genetic algorithm. United States: N. p., 2018. Web. doi:10.1007/S40314-017-0444-Y.
Araújo, Rejane De B., E-mail: rejane.barros@ifpa.edu.br, & Coelho, Antonio A. R.,. Hybridization of IMC and PID control structures based on filtered GPC using genetic algorithm. United States. doi:10.1007/S40314-017-0444-Y.
Araújo, Rejane De B., E-mail: rejane.barros@ifpa.edu.br, and Coelho, Antonio A. R.,. Tue . "Hybridization of IMC and PID control structures based on filtered GPC using genetic algorithm". United States. doi:10.1007/S40314-017-0444-Y.
@article{osti_22769317,
title = {Hybridization of IMC and PID control structures based on filtered GPC using genetic algorithm},
author = {Araújo, Rejane De B., E-mail: rejane.barros@ifpa.edu.br and Coelho, Antonio A. R.,},
abstractNote = {Classical and advanced hybridization methods are employed in the control literature to calibrate the tuning parameters of internal model control (IMC) and proportional-integral-derivative (PID) controllers. This paper presents an alternative tuning design for the generalized predictive controller (GPC) that is based on both the positional process model and cost function. The proposed method involves the selection of an integral polynomial weighting filter for reference and output signals to deal with reference tracking, disturbance rejection, model-plant mismatch (MPM), minimum control energy and closed-loop robustness. The filter positional GPC (FP-GPC) obtained is transformed into a two-degree-of-freedom polynomial filtered RST structure and then into filtered internal model control (F-IMC) and PID controllers. Also, a multi-objective optimization based on genetic algorithm is applied to tune the filter parameters. Numerical and experimental essays show the effectiveness of the proposed control methodology.},
doi = {10.1007/S40314-017-0444-Y},
journal = {Computational and Applied Mathematics},
issn = {0101-8205},
number = 2,
volume = 37,
place = {United States},
year = {2018},
month = {5}
}