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LPV design of charge control for an SI engine based on LFT neural state-space models
Publikationstyp
Conference Paper
Date Issued
2008
Sprache
English
Institut
Journal
Volume
41
Issue
2
Start Page
7427
End Page
7432
Citation
IFAC Proceedings Volumes (IFAC-PapersOnline) 41 (2): 7427-7432 (2008)
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
Elsevier
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.
Subjects
Gain scheduling
Linear parametrically varying (LPV) methodologies
Nonlinear system identification
DDC Class
600: Technik
620: Ingenieurwissenschaften