1. //
  2. Start
  3. //
  4. Publications
  5. Throttle valve control using an inverse ...
  6. Output formats
  7. //

Throttle valve control using an inverse local linear model tree based on a Fuzzy neural network

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

Standard

Throttle valve control using an inverse local linear model tree based on a Fuzzy neural network. / Nentwig, Mirko; Mercorelli, P.
2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008. ed. / Richard A. Comley. IEEE - Institute of Electrical and Electronics Engineers Inc., 2008. p. 234-239 4798943 (2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008).

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

Harvard

Nentwig, M & Mercorelli, P 2008, Throttle valve control using an inverse local linear model tree based on a Fuzzy neural network. in RA Comley (ed.), 2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008., 4798943, 2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008, IEEE - Institute of Electrical and Electronics Engineers Inc., pp. 234-239, 7th Institute of Electrical and Electronics Engineers International Conference on Cybernetic Intelligent Systems - CIS2008, London, United Kingdom, 09.09.08. https://doi.org/10.1109/UKRICIS.2008.4798943

APA

Nentwig, M., & Mercorelli, P. (2008). Throttle valve control using an inverse local linear model tree based on a Fuzzy neural network. In R. A. Comley (Ed.), 2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008 (pp. 234-239). Article 4798943 (2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008). IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/UKRICIS.2008.4798943

Vancouver

Nentwig M, Mercorelli P. Throttle valve control using an inverse local linear model tree based on a Fuzzy neural network. In Comley RA, editor, 2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008. IEEE - Institute of Electrical and Electronics Engineers Inc. 2008. p. 234-239. 4798943. (2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008). doi: 10.1109/UKRICIS.2008.4798943

Bibtex

@inbook{d274db08bb444b40a580bafc8fe6b740,
title = "Throttle valve control using an inverse local linear model tree based on a Fuzzy neural network",
abstract = "A robust throttle valve control has always been an 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 = "By-wire systems, Control loops, Control problems, Control strategies, Data measurements, Feed forwards, Feed-forward controllers, High orders, In controls, Inverse models, Local linear model trees, Noise effects, Simulated results, State variables, Throttle valves, Fuzzy neural networks, Intelligent control, Intelligent systems, Inverse problems, Three term control systems, Mathematical models, Engineering",
author = "Mirko Nentwig and P. Mercorelli",
note = "Conference code: 75856 Export Date: 22 May 2012 Source: Scopus Art. No.: 4798943 doi: 10.1109/UKRICIS.2008.4798943 Language of Original Document: English Correspondence Address: Nentwig, M.; Dep. of Vehicles, Production and Process Engineering, University of Applied Sciences Wolfsburg, Robert-Koch-Platz 8-a, 38440 Wolfsburg, Germany; email: mail@mnentwig.de References: Nelles, O., (1999) Nonlinear System Identification with Local Linear Neuro-Fuzzy Models, , Shaker Verlag, Aachen; Mercorelli, P., An Optimal Minimum Phase Approximating PD Regulator for Robust Control of a Throttle Plate (2006) 45th IEEE Conference on Decision and Control (CDC2006), , San Diego USA; M. Nentwig and P. Mercorelli, A Matlab/Simulink Toolbox for Inversion of Local Linear Model Trees, Introduction to Advanced Scientific Softwares and Toolboxes, IAENG, in pressNakano, K., Modelling and Observer-based Sliding-Mode Control of Electronic Throttle Systems, ECTI Trans. Electrical Eng (2006) Electronics and Communications, 4 (1), pp. 22-28; Rossi, C., Tilli, A., Tonielli, A., Robust control of a throttle body for drive by wire operation of automotive engines (2000) IEEE Trans. Contr. Syst. Technology, 8 (6), pp. 993-1002; Fink, A., Toepfer, S., On the Inversion of Nonlinear Models (2003), Technical ReportFink, A., Toepfer, S., Isermann, R., Nonlinear model-based control with local linear neuro-fuzzy models (2003) Archive of Applied Mechanics, 72 (11-12), pp. 911-922; Fischer, M., Nelles, O., Fink, A., Adaptive Fuzzy Model-based Control (1998) Journal a, 39 (3), pp. 22-28; Fischer, M., Nelles, O., Fink, A., Supervision of Nonlinear Adaptive Controllers Based on Fuzzy Models (1999) 14th IFAC World Congress, Q, pp. 335-340. , Beijing, China; Fink, A., Nelles, O., Fischer, M., Linearization Based and Local Model Based Controller Design (1999) European Control Conference, , ECC, Karlsruhe, Germany; Fink, A., Toepfer, S., Isermann, R., Neuro and Neuro-Fuzzy Identification for Model-based Control (2001) IFAC Workshop on Advanced Fuzzy/Neural Control, Q, pp. 111-116. , Valencia, Spain; Fink, A., Nelles, O., Nonlinear Internal Model Control Based on Local Linear Neural Networks (2001) IEEE Systems, , Man and Cybernetics, Tucson, USA; 7th Institute of Electrical and Electronics Engineers International Conference on Cybernetic Intelligent Systems - CIS2008, CIS2008 ; Conference date: 09-09-2008 Through 10-09-2008",
year = "2008",
month = sep,
day = "1",
doi = "10.1109/UKRICIS.2008.4798943",
language = "English",
isbn = "978-1-4244-2914-1",
series = "2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008",
publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",
pages = "234--239",
editor = "Comley, {Richard A.}",
booktitle = "2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008",
address = "United States",
url = "http://www.cybernetic.org.uk/cis2008",

}

RIS

TY - CHAP

T1 - Throttle valve control using an inverse local linear model tree based on a Fuzzy neural network

AU - Nentwig, Mirko

AU - Mercorelli, P.

N1 - Conference code: 7

PY - 2008/9/1

Y1 - 2008/9/1

N2 - A robust throttle valve control has always been an 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 an 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 - By-wire systems

KW - Control loops

KW - Control problems

KW - Control strategies

KW - Data measurements

KW - Feed forwards

KW - Feed-forward controllers

KW - High orders

KW - In controls

KW - Inverse models

KW - Local linear model trees

KW - Noise effects

KW - Simulated results

KW - State variables

KW - Throttle valves

KW - Fuzzy neural networks

KW - Intelligent control

KW - Intelligent systems

KW - Inverse problems

KW - Three term control systems

KW - Mathematical models

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=64949157801&partnerID=8YFLogxK

U2 - 10.1109/UKRICIS.2008.4798943

DO - 10.1109/UKRICIS.2008.4798943

M3 - Article in conference proceedings

SN - 978-1-4244-2914-1

T3 - 2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008

SP - 234

EP - 239

BT - 2008 7th IEEE International Conference on Cybernetic Intelligent Systems, CIS 2008

A2 - Comley, Richard A.

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - 7th Institute of Electrical and Electronics Engineers International Conference on Cybernetic Intelligent Systems - CIS2008

Y2 - 9 September 2008 through 10 September 2008

ER -

Other publications by the same author(s)

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

Business Analytics and Making Decision Based on Kalman Filter in Stock Prediction Case

Kurniawan, S. & Mercorelli, P., 27.05.2025, 5th Congress on Intelligent Systems, CIS 2024: Volume 1. Kumar, S., Mary Anita, E. A., Kim, J. H. & Nagar, A. (eds.). Springer Science and Business Media Deutschland, p. 309-320 12 p. (Lecture Notes in Networks and Systems; vol. 1275).

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

Dynamic Performance Analysis and Fault Ride-Through Enhancement by a Modified Fault Current Protection Scheme of a Grid-Connected Doubly Fed Induction Generator

Talani, R. A., Kaloi, G. S., Ali, A., Bijarani, M. A., Abbas, G., Hatatah, M., Mercorelli, P. & Touti, E., 02.2025, In: Machines. 13, 2, 20 p., 110.

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

Inner loop model predictive control and outer loop PI reference governor for PMSMs with input and state saturation for torque control

Zwerger, T. & Mercorelli, P., 01.02.2025, In: Mathematics and Computers in Simulation. 228, p. 178-201 24 p.

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

DOI

Recently viewed

Publications

  1. Applying Bayesian Parameter Estimation to A/B Tests in e-Business Applications
  2. Problem structuring for transitions
  3. Diffusion-driven microstructure evolution in OpenCalphad
  4. How to combine collaboration scripts and heuristic worked examples to foster mathematical argumentation - when working memory matters
  5. An Improved Approach to the Semi-Process-Oriented Implementation of Standardised ERP-Systems
  6. Global temporal typing patterns in foreign language writing
  7. Gain Scheduling Controller for Improving Level Control Performance
  8. Retest effects in matrix test performance
  9. Anatomy of Haar Wavelet Filter and Its Implementation for Signal Processing
  10. Template-based Question Answering using Recursive Neural Networks
  11. Sequencing and fading worked examples and collaboration scripts to foster mathematical argumentation - working memory capacity matters for fading
  12. Four Methods to Distinguish between Fractal Dimensions in Time Series through Recurrence Quantification Analysis