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Robust Evolving Cloud-based Controller (ReCCo)

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

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Robust Evolving Cloud-based Controller (ReCCo). / Andonovski, Goran; Angelov, Plamen Parvanov; Blazic, Saso; Skrjanc, Igor.

2017 Evolving and Adaptive Intelligent Systems (EAIS). IEEE, 2017. p. 1-6.

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

Harvard

Andonovski, G, Angelov, PP, Blazic, S & Skrjanc, I 2017, Robust Evolving Cloud-based Controller (ReCCo). in 2017 Evolving and Adaptive Intelligent Systems (EAIS). IEEE, pp. 1-6. https://doi.org/10.1109/EIS2017.7954835

APA

Andonovski, G., Angelov, P. P., Blazic, S., & Skrjanc, I. (2017). Robust Evolving Cloud-based Controller (ReCCo). In 2017 Evolving and Adaptive Intelligent Systems (EAIS) (pp. 1-6). IEEE. https://doi.org/10.1109/EIS2017.7954835

Vancouver

Andonovski G, Angelov PP, Blazic S, Skrjanc I. Robust Evolving Cloud-based Controller (ReCCo). In 2017 Evolving and Adaptive Intelligent Systems (EAIS). IEEE. 2017. p. 1-6 https://doi.org/10.1109/EIS2017.7954835

Author

Andonovski, Goran ; Angelov, Plamen Parvanov ; Blazic, Saso ; Skrjanc, Igor. / Robust Evolving Cloud-based Controller (ReCCo). 2017 Evolving and Adaptive Intelligent Systems (EAIS). IEEE, 2017. pp. 1-6

Bibtex

@inproceedings{2be6b377ed6944288806012ae4db9d76,
title = "Robust Evolving Cloud-based Controller (ReCCo)",
abstract = "This paper presents an autonomous Robust Evolving Cloud-based Controller (RECCo). The control algorithm is a fuzzy type with non-parametric (cloud-based) antecedent part and adaptive PID-R consequent part. The procedure starts with zero clouds (fuzzy rules) and the structure evolves during performing the process control. The PID-R parameters of the first cloud are initialized with zeros and furthermore, they are adapted on-line with a stable adaptation mechanism based on Lyapunov approach. The RECCo controller does not require any mathematical model of the controlled process but just basic information such as input and output range and the estimated value of the dominant time constant. Due to the problem space normalization the design parameters are fixed. The proposed controller with the same initial design parameters was tested on two different simulation examples. The experimental results show the convergence of the adaptive parameters and the effectiveness of the proposed algorithm.",
keywords = "evolving systems, controllers",
author = "Goran Andonovski and Angelov, {Plamen Parvanov} and Saso Blazic and Igor Skrjanc",
note = "{\textcopyright}2017 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 = "2017",
month = jun,
day = "2",
doi = "10.1109/EIS2017.7954835",
language = "English",
isbn = "9781509064458",
pages = "1--6",
booktitle = "2017 Evolving and Adaptive Intelligent Systems (EAIS)",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Robust Evolving Cloud-based Controller (ReCCo)

AU - Andonovski, Goran

AU - Angelov, Plamen Parvanov

AU - Blazic, Saso

AU - Skrjanc, Igor

N1 - ©2017 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 - 2017/6/2

Y1 - 2017/6/2

N2 - This paper presents an autonomous Robust Evolving Cloud-based Controller (RECCo). The control algorithm is a fuzzy type with non-parametric (cloud-based) antecedent part and adaptive PID-R consequent part. The procedure starts with zero clouds (fuzzy rules) and the structure evolves during performing the process control. The PID-R parameters of the first cloud are initialized with zeros and furthermore, they are adapted on-line with a stable adaptation mechanism based on Lyapunov approach. The RECCo controller does not require any mathematical model of the controlled process but just basic information such as input and output range and the estimated value of the dominant time constant. Due to the problem space normalization the design parameters are fixed. The proposed controller with the same initial design parameters was tested on two different simulation examples. The experimental results show the convergence of the adaptive parameters and the effectiveness of the proposed algorithm.

AB - This paper presents an autonomous Robust Evolving Cloud-based Controller (RECCo). The control algorithm is a fuzzy type with non-parametric (cloud-based) antecedent part and adaptive PID-R consequent part. The procedure starts with zero clouds (fuzzy rules) and the structure evolves during performing the process control. The PID-R parameters of the first cloud are initialized with zeros and furthermore, they are adapted on-line with a stable adaptation mechanism based on Lyapunov approach. The RECCo controller does not require any mathematical model of the controlled process but just basic information such as input and output range and the estimated value of the dominant time constant. Due to the problem space normalization the design parameters are fixed. The proposed controller with the same initial design parameters was tested on two different simulation examples. The experimental results show the convergence of the adaptive parameters and the effectiveness of the proposed algorithm.

KW - evolving systems

KW - controllers

U2 - 10.1109/EIS2017.7954835

DO - 10.1109/EIS2017.7954835

M3 - Conference contribution/Paper

SN - 9781509064458

SP - 1

EP - 6

BT - 2017 Evolving and Adaptive Intelligent Systems (EAIS)

PB - IEEE

ER -