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Analysis of adaptation law of the robust evolving cloud-based controller

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

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Analysis of adaptation law of the robust evolving cloud-based controller. / Andonovski, Goran; Blazic, Saso; Angelov, Plamen Parvanov; Skrjanc, Igor.

Proceedings 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS). IEEE, 2015. p. 1-7.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

Harvard

Andonovski, G, Blazic, S, Angelov, PP & Skrjanc, I 2015, Analysis of adaptation law of the robust evolving cloud-based controller. in Proceedings 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS). IEEE, pp. 1-7. https://doi.org/10.1109/EAIS.2015.7368793

APA

Andonovski, G., Blazic, S., Angelov, P. P., & Skrjanc, I. (2015). Analysis of adaptation law of the robust evolving cloud-based controller. In Proceedings 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS) (pp. 1-7). IEEE. https://doi.org/10.1109/EAIS.2015.7368793

Vancouver

Andonovski G, Blazic S, Angelov PP, Skrjanc I. Analysis of adaptation law of the robust evolving cloud-based controller. In Proceedings 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS). IEEE. 2015. p. 1-7 https://doi.org/10.1109/EAIS.2015.7368793

Author

Andonovski, Goran ; Blazic, Saso ; Angelov, Plamen Parvanov ; Skrjanc, Igor. / Analysis of adaptation law of the robust evolving cloud-based controller. Proceedings 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS). IEEE, 2015. pp. 1-7

Bibtex

@inproceedings{239384ca0f61438c9da4821f93fa813b,
title = "Analysis of adaptation law of the robust evolving cloud-based controller",
abstract = "In this paper we propose a performance analysis of the robust evolving cloud-based controller (RECCo) according to the different initial scenarios. RECCo is a controller based on fuzzy rule-based (FRB) systems with non-parametric antecedent part and PID type consequent part. Moreover, the controller structure (the fuzzy rules and the membership function) is created in online manner from the data stream. The advantage of the RECCo controller is that do not require any a priory knowledge of the controlled system. The algorithm starts with zero fuzzy rules (zero data clouds) and evolves/learns during the process control. Also the PID parameters of the controller are initialed with zeros and are adapted in online manner. According to the zero initialization of the parameters the new adaptation law is proposed in this article to solve the problems in the starting phase of the process control. Several initial scenarios were theoretically propagated and experimentally tested on the model of a heat-exchanger plant. These experiments prove that the proposed adaptation law improve the performance of the RECCo control algorithm in the starting phase.",
keywords = "control, evolving, Fuzzy",
author = "Goran Andonovski and Saso Blazic and Angelov, {Plamen Parvanov} and Igor Skrjanc",
note = "{\textcopyright}2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.",
year = "2015",
month = dec,
day = "3",
doi = "10.1109/EAIS.2015.7368793",
language = "English",
pages = "1--7",
booktitle = "Proceedings 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Analysis of adaptation law of the robust evolving cloud-based controller

AU - Andonovski, Goran

AU - Blazic, Saso

AU - Angelov, Plamen Parvanov

AU - Skrjanc, Igor

N1 - ©2015 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2015/12/3

Y1 - 2015/12/3

N2 - In this paper we propose a performance analysis of the robust evolving cloud-based controller (RECCo) according to the different initial scenarios. RECCo is a controller based on fuzzy rule-based (FRB) systems with non-parametric antecedent part and PID type consequent part. Moreover, the controller structure (the fuzzy rules and the membership function) is created in online manner from the data stream. The advantage of the RECCo controller is that do not require any a priory knowledge of the controlled system. The algorithm starts with zero fuzzy rules (zero data clouds) and evolves/learns during the process control. Also the PID parameters of the controller are initialed with zeros and are adapted in online manner. According to the zero initialization of the parameters the new adaptation law is proposed in this article to solve the problems in the starting phase of the process control. Several initial scenarios were theoretically propagated and experimentally tested on the model of a heat-exchanger plant. These experiments prove that the proposed adaptation law improve the performance of the RECCo control algorithm in the starting phase.

AB - In this paper we propose a performance analysis of the robust evolving cloud-based controller (RECCo) according to the different initial scenarios. RECCo is a controller based on fuzzy rule-based (FRB) systems with non-parametric antecedent part and PID type consequent part. Moreover, the controller structure (the fuzzy rules and the membership function) is created in online manner from the data stream. The advantage of the RECCo controller is that do not require any a priory knowledge of the controlled system. The algorithm starts with zero fuzzy rules (zero data clouds) and evolves/learns during the process control. Also the PID parameters of the controller are initialed with zeros and are adapted in online manner. According to the zero initialization of the parameters the new adaptation law is proposed in this article to solve the problems in the starting phase of the process control. Several initial scenarios were theoretically propagated and experimentally tested on the model of a heat-exchanger plant. These experiments prove that the proposed adaptation law improve the performance of the RECCo control algorithm in the starting phase.

KW - control

KW - evolving

KW - Fuzzy

U2 - 10.1109/EAIS.2015.7368793

DO - 10.1109/EAIS.2015.7368793

M3 - Conference contribution/Paper

SP - 1

EP - 7

BT - Proceedings 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS)

PB - IEEE

ER -