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Inversion of fuzzy neural networks for the reduction of noise in the control loop

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Standard

Inversion of fuzzy neural networks for the reduction of noise in the control loop. / Nentwig, Mirko; Mercorelli, Paolo.
Intelligent Manufacturing Systems. ed. / Carlos Eduardo Pereira; Oleg Zaikin; Zbigniew A. Banaszak. Vol. 9 International Federation of Automatic Control, 2008. p. 157-162 (IFAC Proceedings Volumes; Vol. 41, No. 3).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

Harvard

Nentwig, M & Mercorelli, P 2008, Inversion of fuzzy neural networks for the reduction of noise in the control loop. in CE Pereira, O Zaikin & ZA Banaszak (eds), Intelligent Manufacturing Systems. vol. 9, IFAC Proceedings Volumes, no. 3, vol. 41, International Federation of Automatic Control, pp. 157-162, 9th International Federation on Automatic Control Conference on Intelligent Manufacturing Systems - 2008, Szczecin, Poland, 09.10.08. https://doi.org/10.3182/20081205-2-CL-4009.00029

APA

Nentwig, M., & Mercorelli, P. (2008). Inversion of fuzzy neural networks for the reduction of noise in the control loop. In C. E. Pereira, O. Zaikin, & Z. A. Banaszak (Eds.), Intelligent Manufacturing Systems (Vol. 9, pp. 157-162). (IFAC Proceedings Volumes; Vol. 41, No. 3). International Federation of Automatic Control. https://doi.org/10.3182/20081205-2-CL-4009.00029

Vancouver

Nentwig M, Mercorelli P. Inversion of fuzzy neural networks for the reduction of noise in the control loop. In Pereira CE, Zaikin O, Banaszak ZA, editors, Intelligent Manufacturing Systems. Vol. 9. International Federation of Automatic Control. 2008. p. 157-162. (IFAC Proceedings Volumes; 3). doi: 10.3182/20081205-2-CL-4009.00029

Bibtex

@inbook{6517e090174944509d27319c1e5f052f,
title = "Inversion of fuzzy neural networks for the reduction of noise in the control loop",
abstract = "A robust throttle valve control has always been a attractive problem since throttle by wire systems were established in the mid-nineties. Often in control strategy, a feedforward controller is adopted in which an inverse model is used. Mathematical inversions of models imply a high order of differentiation of the state variables and consequently noise effects. In general, neural networks are a very effective and popular tool mostly used for modeling. The inversion of a neural network produces real possibilities to involve the networks in the control problem schemes. This paper presents a control strategy based upon an inversion of a feed forward trained local linear model tree. The local linear model tree is realized through a fuzzy neural network. Simulated results from real data measurements are presented in which two control loops are explicitly compared",
keywords = "Engineering, Fuzzy networks, inversion, noise reduction",
author = "Mirko Nentwig and Paolo Mercorelli",
year = "2008",
doi = "10.3182/20081205-2-CL-4009.00029",
language = "English",
isbn = "978-3-902661-40-1",
volume = "9",
series = "IFAC Proceedings Volumes",
publisher = "International Federation of Automatic Control",
number = "3",
pages = "157--162",
editor = "Pereira, {Carlos Eduardo} and Oleg Zaikin and Banaszak, {Zbigniew A.}",
booktitle = "Intelligent Manufacturing Systems",
address = "Austria",
note = "9th International Federation on Automatic Control Conference on Intelligent Manufacturing Systems - 2008, 9th IFAC Conference on Intelligent Manufacturing Systems - 2008 ; Conference date: 09-10-2008 Through 10-10-2008",
url = "http://www.ifacims2019.com/",

}

RIS

TY - CHAP

T1 - Inversion of fuzzy neural networks for the reduction of noise in the control loop

AU - Nentwig, Mirko

AU - Mercorelli, Paolo

N1 - Conference code: 9

PY - 2008

Y1 - 2008

N2 - A robust throttle valve control has always been a attractive problem since throttle by wire systems were established in the mid-nineties. Often in control strategy, a feedforward controller is adopted in which an inverse model is used. Mathematical inversions of models imply a high order of differentiation of the state variables and consequently noise effects. In general, neural networks are a very effective and popular tool mostly used for modeling. The inversion of a neural network produces real possibilities to involve the networks in the control problem schemes. This paper presents a control strategy based upon an inversion of a feed forward trained local linear model tree. The local linear model tree is realized through a fuzzy neural network. Simulated results from real data measurements are presented in which two control loops are explicitly compared

AB - A robust throttle valve control has always been a attractive problem since throttle by wire systems were established in the mid-nineties. Often in control strategy, a feedforward controller is adopted in which an inverse model is used. Mathematical inversions of models imply a high order of differentiation of the state variables and consequently noise effects. In general, neural networks are a very effective and popular tool mostly used for modeling. The inversion of a neural network produces real possibilities to involve the networks in the control problem schemes. This paper presents a control strategy based upon an inversion of a feed forward trained local linear model tree. The local linear model tree is realized through a fuzzy neural network. Simulated results from real data measurements are presented in which two control loops are explicitly compared

KW - Engineering

KW - Fuzzy networks

KW - inversion

KW - noise reduction

U2 - 10.3182/20081205-2-CL-4009.00029

DO - 10.3182/20081205-2-CL-4009.00029

M3 - Article in conference proceedings

SN - 978-3-902661-40-1

VL - 9

T3 - IFAC Proceedings Volumes

SP - 157

EP - 162

BT - Intelligent Manufacturing Systems

A2 - Pereira, Carlos Eduardo

A2 - Zaikin, Oleg

A2 - Banaszak, Zbigniew A.

PB - International Federation of Automatic Control

T2 - 9th International Federation on Automatic Control Conference on Intelligent Manufacturing Systems - 2008

Y2 - 9 October 2008 through 10 October 2008

ER -

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Advancing Sustainability and Productivity: The Role of Precision Agriculture in Vineyards and Olive Groves

Fernandes, F. M., Dos Santos, M. F., De Morais, M. H. F., Lima, J., Pereira, A. I. & Mercorelli, P., 2025, Proceedings of the 2025 26th International Carpathian Control Conference (ICCC): Grandhotel Starý Smokovec, High Tatras, Starý Smokovec, Slovak Republic, 19-21 May 2025. Kacur, J., Skovranek, T., Laciak, M. & Mojzisova, A. (eds.). Piscataway: Institute of Electrical and Electronics Engineers Inc., 6 p. (International Carpathian Control Conference, ICCC; vol. 26).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

A Fluid Elastomeric Actuator Design for Soft Robots

Els, D., van Niekerk, T., Mercorelli, P. & Welgemoed, J., 09.2025, In: Actuators. 14, 9, 14 p., 417.

Research output: Journal contributions › Journal articles › Research › peer-review

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Li, X., Zhang, Q., Gu, H., Othmen, S., Asklany, S., Lhioui, C., Elrashidi, A. & Mercorelli, P., 07.2025, In: PLoS ONE. 20, 7, 22 p., e0327249.

Research output: Journal contributions › Journal articles › Research › peer-review

An Over-Actuated Hexacopter Tilt-Rotor UAV Prototype for Agriculture of Precision: Modeling and Control

Pimentel, G. O., Santos, M. F. D., Lima, J., Mercorelli, P. & Fernandes, F. M., 15.01.2025, In: Sensors. 25, 2, 27 p., 479.

Research output: Journal contributions › Journal articles › Research › peer-review

Automatic Tuning of Extended Kalman Filter in Synchronous Reluctance Motor Drives with a Master-Slave Configuration

Rigon, S., Haus, B., Mercorelli, P. & Zigliotto, M., 27.02.2025, In: IEEE Open Journal of Power Electronics. 6, p. 734-746 13 p.

Research output: Journal contributions › Journal articles › Research › peer-review

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