Home > Research > Publications & Outputs > A robust evolving cloud-based controller

Links

Text available via DOI:

Keywords

View graph of relations

A robust evolving cloud-based controller

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter (peer-reviewed)peer-review

Published

Standard

A robust evolving cloud-based controller. / Angelov, Plamen; Skrjanc, Igor; Blazic, Saso.

Springer Handbook of Computational Intelligence . ed. / Janusz Kacprzyk; Witold Pedrycz. Vol. G Berlin : Springer, 2015. p. 1435-1449.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter (peer-reviewed)peer-review

Harvard

Angelov, P, Skrjanc, I & Blazic, S 2015, A robust evolving cloud-based controller. in J Kacprzyk & W Pedrycz (eds), Springer Handbook of Computational Intelligence . vol. G, Springer, Berlin, pp. 1435-1449. https://doi.org/10.1007/978-3-662-43505-2_75

APA

Angelov, P., Skrjanc, I., & Blazic, S. (2015). A robust evolving cloud-based controller. In J. Kacprzyk, & W. Pedrycz (Eds.), Springer Handbook of Computational Intelligence (Vol. G, pp. 1435-1449). Springer. https://doi.org/10.1007/978-3-662-43505-2_75

Vancouver

Angelov P, Skrjanc I, Blazic S. A robust evolving cloud-based controller. In Kacprzyk J, Pedrycz W, editors, Springer Handbook of Computational Intelligence . Vol. G. Berlin: Springer. 2015. p. 1435-1449 https://doi.org/10.1007/978-3-662-43505-2_75

Author

Angelov, Plamen ; Skrjanc, Igor ; Blazic, Saso. / A robust evolving cloud-based controller. Springer Handbook of Computational Intelligence . editor / Janusz Kacprzyk ; Witold Pedrycz. Vol. G Berlin : Springer, 2015. pp. 1435-1449

Bibtex

@inbook{f69b4a4ce36249bd8f8a56315b54ff0d,
title = "A robust evolving cloud-based controller",
abstract = "In this chapter a novel online self-evolving cloud-based controller, called Robust Evolving Cloud-based Controller (RECCo ) is introduced. This type of controller has a parameter-free antecedent (IF) part, a locally valid PID consequent part, and a center-of-gravity based defuzzification. A first-order learning method is applied to consequent parameters and reference model adaptive control is used locally in the ANYA type fuzzy rule-based system. An illustrative example is provided mainly for a proof of concept. The proposed controller can start with no pre-defined fuzzy rules and does not need to pre-define the range of the output, number of rules, membership functions, or connectives such as AND, OR. This RECCo controller learns autonomously from its own actions while controlling the plant. It does not use any off-line pre-training or explicit models (e. g. in the form of differential equations) of the plant. It has been demonstrated that it is possible to fully autonomously and in an unsupervised manner (based only on the data density and selecting representative prototypes/focal points from the control hypersurface acting as a data space) generate and self-tune/learn a non-linear controller structure and evolve it in online mode. Moreover, the results demonstrate that this autonomous controller has no parameters in the antecedent part and surpasses both traditional PID controllers being a non-linear, fuzzy combination of locally valid PID controllers, as well as traditional fuzzy (Mamdani and Takagi–Sugeno) type controllers by their lean structure and higher performance, lack of membership functions, antecedent parameters, and because they do not need off-line tuning.",
keywords = "evolving, control",
author = "Plamen Angelov and Igor Skrjanc and Saso Blazic",
year = "2015",
doi = "10.1007/978-3-662-43505-2_75",
language = "English",
isbn = "9783662435045",
volume = "G",
pages = "1435--1449",
editor = "Janusz Kacprzyk and Witold Pedrycz",
booktitle = "Springer Handbook of Computational Intelligence",
publisher = "Springer",

}

RIS

TY - CHAP

T1 - A robust evolving cloud-based controller

AU - Angelov, Plamen

AU - Skrjanc, Igor

AU - Blazic, Saso

PY - 2015

Y1 - 2015

N2 - In this chapter a novel online self-evolving cloud-based controller, called Robust Evolving Cloud-based Controller (RECCo ) is introduced. This type of controller has a parameter-free antecedent (IF) part, a locally valid PID consequent part, and a center-of-gravity based defuzzification. A first-order learning method is applied to consequent parameters and reference model adaptive control is used locally in the ANYA type fuzzy rule-based system. An illustrative example is provided mainly for a proof of concept. The proposed controller can start with no pre-defined fuzzy rules and does not need to pre-define the range of the output, number of rules, membership functions, or connectives such as AND, OR. This RECCo controller learns autonomously from its own actions while controlling the plant. It does not use any off-line pre-training or explicit models (e. g. in the form of differential equations) of the plant. It has been demonstrated that it is possible to fully autonomously and in an unsupervised manner (based only on the data density and selecting representative prototypes/focal points from the control hypersurface acting as a data space) generate and self-tune/learn a non-linear controller structure and evolve it in online mode. Moreover, the results demonstrate that this autonomous controller has no parameters in the antecedent part and surpasses both traditional PID controllers being a non-linear, fuzzy combination of locally valid PID controllers, as well as traditional fuzzy (Mamdani and Takagi–Sugeno) type controllers by their lean structure and higher performance, lack of membership functions, antecedent parameters, and because they do not need off-line tuning.

AB - In this chapter a novel online self-evolving cloud-based controller, called Robust Evolving Cloud-based Controller (RECCo ) is introduced. This type of controller has a parameter-free antecedent (IF) part, a locally valid PID consequent part, and a center-of-gravity based defuzzification. A first-order learning method is applied to consequent parameters and reference model adaptive control is used locally in the ANYA type fuzzy rule-based system. An illustrative example is provided mainly for a proof of concept. The proposed controller can start with no pre-defined fuzzy rules and does not need to pre-define the range of the output, number of rules, membership functions, or connectives such as AND, OR. This RECCo controller learns autonomously from its own actions while controlling the plant. It does not use any off-line pre-training or explicit models (e. g. in the form of differential equations) of the plant. It has been demonstrated that it is possible to fully autonomously and in an unsupervised manner (based only on the data density and selecting representative prototypes/focal points from the control hypersurface acting as a data space) generate and self-tune/learn a non-linear controller structure and evolve it in online mode. Moreover, the results demonstrate that this autonomous controller has no parameters in the antecedent part and surpasses both traditional PID controllers being a non-linear, fuzzy combination of locally valid PID controllers, as well as traditional fuzzy (Mamdani and Takagi–Sugeno) type controllers by their lean structure and higher performance, lack of membership functions, antecedent parameters, and because they do not need off-line tuning.

KW - evolving

KW - control

U2 - 10.1007/978-3-662-43505-2_75

DO - 10.1007/978-3-662-43505-2_75

M3 - Chapter (peer-reviewed)

SN - 9783662435045

VL - G

SP - 1435

EP - 1449

BT - Springer Handbook of Computational Intelligence

A2 - Kacprzyk, Janusz

A2 - Pedrycz, Witold

PB - Springer

CY - Berlin

ER -