State-Space Control of Nonlinear Systems Identified by ANARX and Neural Network based SANARX Models

被引：0
|
作者：
Vassiljeva, K. [1 ]
Petlenkov, E. [1 ]
Belikov, J. [2 ]
机构：
[1] Tallinn Univ Technol, Dept Comp Control, EE-19086 Tallinn, Estonia
[2] Tallinn Univ Technol, Inst Cybernet, EE-12618 Tallinn, Estonia
来源：
2010 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS IJCNN 2010 | 2010年
关键词：
state-space control; nonlinear control systems; ANARX model; neural networks and dynamic feedback linearization; REALIZABILITY; DESIGN;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
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页数：8
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