State-space control of nonlinear systems identified by ANARX and Neural Network based SANARX models

Author

Vassiljeva, K. ; Petlenkov, E. ; Belikov, J.

Author_Institution

Dept. of Comput. Control, Tallinn Univ. of Technol., Tallinn, Estonia

fYear

2010

fDate

18-23 July 2010

Firstpage

1

Lastpage

8

Abstract

A state-space technique for control of nonlinear SISO systems identified by an Additive Nonlinear Autoregressive eXogenous (ANARX) model is presented. Two cases are shown. In the first case system model is given explicitly in the form of ANARX structure. In the second case controlled system is identified by Neural Network based Simplified Additive NARX (NN-SANARX) model linearized by dynamic feedback. The neural network based model is represented in the discrete-time state-space form. The effectiveness of the approach proposed in the paper is demonstrated on numerical examples with SISO and MIMO systems.

Keywords

MIMO systems; autoregressive processes; discrete time systems; feedback; neurocontrollers; nonlinear control systems; state-space methods; ANARX structure; MIMO system; NN-SANARX model; additive nonlinear autoregressive exogenous model; discrete time state space; dynamic feedback; neural network based SANARX model; neural network based simplified additive NARX; nonlinear SISO systems; Artificial neural networks; Computational modeling; Control systems; Equations; Mathematical model; Nonlinear systems; Numerical models; ANARX model; neural networks and dynamic feedback linearization; nonlinear control systems; state-space control;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks (IJCNN), The 2010 International Joint Conference on

Conference_Location

Barcelona

ISSN

1098-7576

Print_ISBN

978-1-4244-6916-1

Type

conf

DOI

10.1109/IJCNN.2010.5596581

Filename

5596581