Stable nonlinear receding horizon regulator using RBF neural network models

Zahir Ahmida; Abdelfatah Charef; Victor Becerra

doi:10.1109/MED.2006.328824

Stable nonlinear receding horizon regulator using RBF neural network models

Zahir Ahmida, Abdelfatah Charef, Victor Becerra

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The general stability theory of nonlinear receding horizon controllers has attracted much attention over the last fifteen years, and many algorithms have been proposed to ensure closed-loop stability. On the other hand many reports exist regarding the use of artificial neural network models in nonlinear receding horizon control. However, little attention has been given to the stability issue of these specific controllers. This paper addresses this problem and proposes to cast the nonlinear receding horizon control based on neural network models within the framework of an existing stabilising algorithm.

Original language	English
Title of host publication	14th Mediterranean Conference on Control and Automation, 2006
Subtitle of host publication	MED '06
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-5
ISBN (Electronic)	0978672003
ISBN (Print)	0978672011
DOIs	https://doi.org/10.1109/MED.2006.328824
Publication status	Published - 2006

Keywords

RBF neural network model, closed-loop stability, nonlinear receding horizon controller, stabilisation algorithm, stability theory, stable nonlinear receding horizon regulator

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10.1109/MED.2006.328824

Cite this

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title = "Stable nonlinear receding horizon regulator using RBF neural network models",

abstract = "The general stability theory of nonlinear receding horizon controllers has attracted much attention over the last fifteen years, and many algorithms have been proposed to ensure closed-loop stability. On the other hand many reports exist regarding the use of artificial neural network models in nonlinear receding horizon control. However, little attention has been given to the stability issue of these specific controllers. This paper addresses this problem and proposes to cast the nonlinear receding horizon control based on neural network models within the framework of an existing stabilising algorithm.",

keywords = "RBF neural network model, closed-loop stability, nonlinear receding horizon controller, stabilisation algorithm, stability theory, stable nonlinear receding horizon regulator",

author = "Zahir Ahmida and Abdelfatah Charef and Victor Becerra",

year = "2006",

doi = "10.1109/MED.2006.328824",

language = "English",

isbn = "0978672011",

pages = "1--5",

booktitle = "14th Mediterranean Conference on Control and Automation, 2006",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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}

Stable nonlinear receding horizon regulator using RBF neural network models. / Ahmida, Zahir; Charef, Abdelfatah; Becerra, Victor.
14th Mediterranean Conference on Control and Automation, 2006: MED '06. Piscataway: Institute of Electrical and Electronics Engineers Inc., 2006. p. 1-5.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Stable nonlinear receding horizon regulator using RBF neural network models

AU - Ahmida, Zahir

AU - Charef, Abdelfatah

AU - Becerra, Victor

PY - 2006

Y1 - 2006

N2 - The general stability theory of nonlinear receding horizon controllers has attracted much attention over the last fifteen years, and many algorithms have been proposed to ensure closed-loop stability. On the other hand many reports exist regarding the use of artificial neural network models in nonlinear receding horizon control. However, little attention has been given to the stability issue of these specific controllers. This paper addresses this problem and proposes to cast the nonlinear receding horizon control based on neural network models within the framework of an existing stabilising algorithm.

AB - The general stability theory of nonlinear receding horizon controllers has attracted much attention over the last fifteen years, and many algorithms have been proposed to ensure closed-loop stability. On the other hand many reports exist regarding the use of artificial neural network models in nonlinear receding horizon control. However, little attention has been given to the stability issue of these specific controllers. This paper addresses this problem and proposes to cast the nonlinear receding horizon control based on neural network models within the framework of an existing stabilising algorithm.

KW - RBF neural network model, closed-loop stability, nonlinear receding horizon controller, stabilisation algorithm, stability theory, stable nonlinear receding horizon regulator

U2 - 10.1109/MED.2006.328824

DO - 10.1109/MED.2006.328824

M3 - Conference contribution

SN - 0978672011

SP - 1

EP - 5

BT - 14th Mediterranean Conference on Control and Automation, 2006

PB - Institute of Electrical and Electronics Engineers Inc.

CY - Piscataway

ER -

Stable nonlinear receding horizon regulator using RBF neural network models

Abstract

Keywords

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