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
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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
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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 -