@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",
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",
}
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
Y1 - 2008
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 -
