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LPV design of charge control for an SI engine based on LFT neural state-space models
- Link:
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- Autor/in:
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- Verlag/Körperschaft:
- Hamburg University of Technology
- Erscheinungsjahr:
- 2008
- Medientyp:
- Text
- Schlagworte:
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- Gain scheduling
- Linear parametrically varying (LPV) methodologies
- Nonlinear system identification
- 600: Technik
- 620: Ingenieurwissenschaften
- Beschreibung:
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- This paper is one of two joint papers, each presenting and utilizing a different representation of a feedforward neural network for controller design. Here a neural state-space model is transformed into a linear fractional transformation (LFT) representation to obtain a discrete-time quasi-linear parameter-varying (LPV) model of a nonlinear plant, whereas in the joint paper (Abbas and Werner [2008]) a method is proposed to transform the neural state-space into a discrete-time polytopic quasi-LPV model. As a practical application, air charge control of a Spark-Ignition (SI) engine is used in both papers as example to illustrate two different synthesis methods for fixed structure low-order discrete-time LPV controllers. In this paper, a method that combines modelling using a multilayer perceptron network and controller synthesis using linear matrix inequalities (LMIs) and evolutionary search is proposed. In the first step a neural state-space model is transformed into a linear fractional transformation (LFT) representation to obtain a discrete-time quasi-LPV model of a nonlinear plant from input-output data only. Then a hybrid approach using LMI solvers and genetic algorithm, which is based on the concept of quadratic separators, is used to synthesize a discrete-time LPV controller. Copyright © 2007 International Federation of Automatic Control All Rights Reserved.
- Beziehungen:
- DOI 10.3182/20080706-5-KR-1001.01255
- Quellsystem:
- TUHH Open Research
Interne Metadaten
- Quelldatensatz
- oai:tore.tuhh.de:11420/14838