Title :
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
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;
Conference_Titel :
Neural Networks (IJCNN), The 2010 International Joint Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-1-4244-6916-1
DOI :
10.1109/IJCNN.2010.5596581
