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.
引用
收藏
页数：8
相关论文
共 50 条
  • [1] Neural Network Based Minimal State-Space Representation of Nonlinear MIMO Systems for Feedback Control
    Vassiljeva, Kristina
    Petlenkov, Eduard
    Belikov, Juri
    11TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV 2010), 2010, : 2191 - 2196
  • [2] Computationally Efficient Nonlinear Predictive Control Based on State-Space Neural Models
    Lawrynczuk, Maciej
    PARALLEL PROCESSING AND APPLIED MATHEMATICS, PT I, 2010, 6067 : 350 - 359
  • [3] A STATE-SPACE NEURAL NETWORK FOR MODELING DYNAMICAL NONLINEAR SYSTEMS
    Amoura, Karima
    Wira, Patrice
    Djennoune, Said
    NCTA 2011: PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON NEURAL COMPUTATION THEORY AND APPLICATIONS, 2011, : 369 - 376
  • [4] Model Reference Control of Nonlinear Systems by Dynamic Output Feedback Linearization of Neural Network based ANARX Models
    Petlenkov, Eduard
    2008 10TH INTERNATIONAL CONFERENCE ON CONTROL AUTOMATION ROBOTICS & VISION: ICARV 2008, VOLS 1-4, 2008, : 1119 - 1123
  • [5] Model Reference Control of Nonlinear TITO Systems by Dynamic Output Feedback Linearization of Neural Network based ANARX Models
    Belikov, Juri
    Petlenkov, Eduard
    2009 IEEE CONTROL APPLICATIONS CCA & INTELLIGENT CONTROL (ISIC), VOLS 1-3, 2009, : 1820 - +
  • [6] Learning nonlinear state-space models for control
    Raiko, T
    Tornio, M
    PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), VOLS 1-5, 2005, : 815 - 820
  • [7] Identification of structured nonlinear state-space models for hysteretic systems using neural network hysteresis operators
    Krikelis, Konstantinos
    Pei, Jin-Song
    van Berkel, Koos
    Schoukens, Maarten
    MEASUREMENT, 2024, 224
  • [8] NN-ANARX structure for control of nonlinear SISO and MIMO systems: Neural network based approach
    Petlenkov, Eduard
    Belikov, Juri
    PROCEEDINGS OF THE 26TH CHINESE CONTROL CONFERENCE, VOL 4, 2007, : 138 - 144
  • [9] State-space neural network for modelling, prediction and control
    Zamarreño, JM
    Vega, P
    García, LD
    Francisco, M
    CONTROL ENGINEERING PRACTICE, 2000, 8 (09) : 1063 - 1075
  • [10] An Approach to the Design of Nonlinear State-Space Control Systems
    Pozna, Claudiu
    Precup, Radu-Emil
    STUDIES IN INFORMATICS AND CONTROL, 2018, 27 (01): : 5 - 14
12345