Predictive Controller Based on Fuzzy-Neural model with multiple recurrent nodes

Margarita Terziyska; Yancho Todorov; Michail Petrov

Predictive Controller Based on Fuzzy-Neural model with multiple recurrent nodes

Margarita Terziyska, Yancho Todorov, Michail Petrov

Not published at Aalto University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

Abstract

A nonlinear predictive controller based on a recurrent neuro-fuzzy model is presented in this paper. Neuro-fuzzy model is realized with the T-S inference mechanism and includes global and local (after the rules layer) feedbacks. The proposed model is coupled with an optimization approach for computation of the control actions into a model based predictive controller. The efficiency of the proposed control policy is proved by simulation experiments to control a Continuous Stirred Tank Reactor (CSTR).

Original language	English
Title of host publication	International Conference "FOOD SCIENCE, ENGINEERING AND TECHNOLOGIES – 2013“, 18-19 October 2013, Plovdiv
Pages	63-68
Volume	LX
Publication status	Published - 2013
MoE publication type	A4 Article in a conference publication
Event	Food Science, Engineering and Technologies - Plovdiv, Bulgaria Duration: 18 Oct 2013 → 19 Oct 2013

Conference

Conference	Food Science, Engineering and Technologies
Country	Bulgaria
City	Plovdiv
Period	18/10/2013 → 19/10/2013

Keywords

process control
artificial intelligence
fuzzy systems
neural networks
optimizaiton

Access to Document

http://ytodorov.eu/2013/07_M_Terziiska.pdf

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Cite this

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title = "Predictive Controller Based on Fuzzy-Neural model with multiple recurrent nodes",

abstract = "A nonlinear predictive controller based on a recurrent neuro-fuzzy model is presented in this paper. Neuro-fuzzy model is realized with the T-S inference mechanism and includes global and local (after the rules layer) feedbacks. The proposed model is coupled with an optimization approach for computation of the control actions into a model based predictive controller. The efficiency of the proposed control policy is proved by simulation experiments to control a Continuous Stirred Tank Reactor (CSTR).",

keywords = "process control, artificial intelligence, fuzzy systems, neural networks, optimizaiton",

author = "Margarita Terziyska and Yancho Todorov and Michail Petrov",

year = "2013",

language = "English",

isbn = "1314-7102",

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booktitle = "International Conference {"}FOOD SCIENCE, ENGINEERING AND TECHNOLOGIES – 2013“, 18-19 October 2013, Plovdiv",

note = "Food Science, Engineering and Technologies ; Conference date: 18-10-2013 Through 19-10-2013",

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Terziyska, M, Todorov, Y & Petrov, M 2013, Predictive Controller Based on Fuzzy-Neural model with multiple recurrent nodes. in International Conference "FOOD SCIENCE, ENGINEERING AND TECHNOLOGIES – 2013“, 18-19 October 2013, Plovdiv. vol. LX, pp. 63-68, Food Science, Engineering and Technologies, Plovdiv, Bulgaria, 18/10/2013. <http://ytodorov.eu/2013/07_M_Terziiska.pdf>

Predictive Controller Based on Fuzzy-Neural model with multiple recurrent nodes. / Terziyska, Margarita; Todorov, Yancho; Petrov, Michail.

International Conference "FOOD SCIENCE, ENGINEERING AND TECHNOLOGIES – 2013“, 18-19 October 2013, Plovdiv. Vol. LX 2013. p. 63-68.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Predictive Controller Based on Fuzzy-Neural model with multiple recurrent nodes

AU - Terziyska, Margarita

AU - Todorov, Yancho

AU - Petrov, Michail

PY - 2013

Y1 - 2013

N2 - A nonlinear predictive controller based on a recurrent neuro-fuzzy model is presented in this paper. Neuro-fuzzy model is realized with the T-S inference mechanism and includes global and local (after the rules layer) feedbacks. The proposed model is coupled with an optimization approach for computation of the control actions into a model based predictive controller. The efficiency of the proposed control policy is proved by simulation experiments to control a Continuous Stirred Tank Reactor (CSTR).

AB - A nonlinear predictive controller based on a recurrent neuro-fuzzy model is presented in this paper. Neuro-fuzzy model is realized with the T-S inference mechanism and includes global and local (after the rules layer) feedbacks. The proposed model is coupled with an optimization approach for computation of the control actions into a model based predictive controller. The efficiency of the proposed control policy is proved by simulation experiments to control a Continuous Stirred Tank Reactor (CSTR).

KW - process control

KW - artificial intelligence

KW - fuzzy systems

KW - neural networks

KW - optimizaiton

M3 - Conference contribution

SN - 1314-7102

VL - LX

SP - 63

EP - 68

BT - International Conference "FOOD SCIENCE, ENGINEERING AND TECHNOLOGIES – 2013“, 18-19 October 2013, Plovdiv

T2 - Food Science, Engineering and Technologies

Y2 - 18 October 2013 through 19 October 2013

ER -