Abbas, Hossam El-Din Mahmoud SeddikHossam El-Din Mahmoud SeddikAbbasWerner, HerbertHerbertWerner2023-02-162023-02-162008IFAC Proceedings Volumes (IFAC-PapersOnline) 41 (2): 7427-7432 (2008)http://hdl.handle.net/11420/14838This 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.en1474-66702008274277432ElsevierGain schedulingLinear parametrically varying (LPV) methodologiesNonlinear system identificationTechnikIngenieurwissenschaftenConference Paper10.3182/20080706-5-KR-1001.01255Conference Paper