A Deep Recurrent Neural Network Model for affine quasi-LPV System Identification

Abstract

This paper presents a new model structure and structure selection procedure for the identification of control-oriented affine quasi-LPV (qLPV) models in state-space (SS) representation using deep Recurrent Neural Networks (RNNs). The proposed model structure is intended to be an alternative to the existing black-box approaches [1], [2] for the case where the scheduling variables depend on internal states of the system, which are assumed to be unknown. Existing identification approaches are not able to incorporate deep neural network (DNN) structures but employ (normalized) radial basis functions (RBFs) to model the dependence of the time-varying parameters on the scheduling variables. This may increase the dimension of the time-varying parameter vector unnecessarily, making parameter estimation more difficult and limiting the model's use for LPV controller synthesis. The proposed identification approach aims to reduce the required dimension of the time-varying parameter vector by using a (Deep) Neural Network (NN) to model the parameter variation. In order to curb the complexity of the resulting nonlinear optimization problem and make the developed model approach useful in real-life applications, a structure selection procedure based on an initialization method developed in [3] is proposed. The performance of the presented approach is demonstrated on a nonlinear system identification problem.