Distributed fuzzy-neural state-space predictive control

Yancho Todorov; Margarita Terziyska; Luybka Doukovska

doi:10.1109/PC.2015.7169934

Distributed fuzzy-neural state-space predictive control

Yancho Todorov, Margarita Terziyska, Luybka Doukovska

Not published at VTT

Bulgarian Academy of Sciences

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

3 Citations (Scopus)

Abstract

This paper describes the development of nonlinear state-space predictive controller based on distributed fuzzyneural model. The presented approach assumes a state-space representation in order to obtain more compact form of the model, without statement of a great number of parameters needed to represent nonlinear relations. To increase the flexibility of the network, a set of fuzzy inferences is used to estimate the current system states, as well as to construct a simple predictor needed to update the future system behavior along the defined horizons. At each sampling period an optimization task performing Quadratic Programming minimization assuming the imposed constraints on the system parameters is solved. The performance of the proposed controller is assessed by simulation experiments in modeling and control of nonlinear systems with complicated dynamics.

Original language	English
Title of host publication	Proceedings of the 2015 20th International Conference on Process Control, PC 2015
Editors	M. Fikar, M. Kvasnica
Publisher	IEEE Institute of Electrical and Electronic Engineers
Pages	31-36
ISBN (Print)	978-1-4673-6627-4
DOIs	https://doi.org/10.1109/PC.2015.7169934
Publication status	Published - 28 Jul 2015
MoE publication type	A4 Article in a conference publication
Event	20th International Conference on Process Control, PC 2015 - Strbske Pleso, Slovakia Duration: 9 Jun 2015 → 12 Jun 2015

Conference

Conference	20th International Conference on Process Control, PC 2015
Country/Territory	Slovakia
City	Strbske Pleso
Period	9/06/15 → 12/06/15

Keywords

Distributed models
Fuzzy-neural networks
Model predictive control
Statespace systems

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10.1109/PC.2015.7169934

Cite this

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title = "Distributed fuzzy-neural state-space predictive control",

abstract = "This paper describes the development of nonlinear state-space predictive controller based on distributed fuzzyneural model. The presented approach assumes a state-space representation in order to obtain more compact form of the model, without statement of a great number of parameters needed to represent nonlinear relations. To increase the flexibility of the network, a set of fuzzy inferences is used to estimate the current system states, as well as to construct a simple predictor needed to update the future system behavior along the defined horizons. At each sampling period an optimization task performing Quadratic Programming minimization assuming the imposed constraints on the system parameters is solved. The performance of the proposed controller is assessed by simulation experiments in modeling and control of nonlinear systems with complicated dynamics.",

keywords = "Distributed models, Fuzzy-neural networks, Model predictive control, Statespace systems",

author = "Yancho Todorov and Margarita Terziyska and Luybka Doukovska",

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doi = "10.1109/PC.2015.7169934",

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booktitle = "Proceedings of the 2015 20th International Conference on Process Control, PC 2015",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

address = "United States",

note = "20th International Conference on Process Control, PC 2015 ; Conference date: 09-06-2015 Through 12-06-2015",

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Todorov, Y, Terziyska, M & Doukovska, L 2015, Distributed fuzzy-neural state-space predictive control. in M Fikar & M Kvasnica (eds), Proceedings of the 2015 20th International Conference on Process Control, PC 2015., 7169934, IEEE Institute of Electrical and Electronic Engineers, pp. 31-36, 20th International Conference on Process Control, PC 2015, Strbske Pleso, Slovakia, 9/06/15. https://doi.org/10.1109/PC.2015.7169934

Distributed fuzzy-neural state-space predictive control. / Todorov, Yancho; Terziyska, Margarita; Doukovska, Luybka.

Proceedings of the 2015 20th International Conference on Process Control, PC 2015. ed. / M. Fikar; M. Kvasnica. IEEE Institute of Electrical and Electronic Engineers, 2015. p. 31-36 7169934.

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

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AB - This paper describes the development of nonlinear state-space predictive controller based on distributed fuzzyneural model. The presented approach assumes a state-space representation in order to obtain more compact form of the model, without statement of a great number of parameters needed to represent nonlinear relations. To increase the flexibility of the network, a set of fuzzy inferences is used to estimate the current system states, as well as to construct a simple predictor needed to update the future system behavior along the defined horizons. At each sampling period an optimization task performing Quadratic Programming minimization assuming the imposed constraints on the system parameters is solved. The performance of the proposed controller is assessed by simulation experiments in modeling and control of nonlinear systems with complicated dynamics.

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KW - Statespace systems

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Distributed fuzzy-neural state-space predictive control

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