Robust output tracking of transverse flux machines using RBF neural network

被引：0
作者：
Karimi, HR [1 ]
Babazadeh, A [1 ]
Parspour, N [1 ]
机构：
[1] Univ Teheran, Fac Engn, Dept Elect & Comp Engn, Tehran, Iran
来源：
2004 IEEE CONFERENCE ON ROBOTICS, AUTOMATION AND MECHATRONICS, VOLS 1 AND 2 | 2004年
关键词：
RBF network; output tracking; robust control; transverse flux machine;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents an application of Radial Basis Function (RBF) in identification and control design of Transverse Flux Machines as nonlinear systems with unknown nonlinearity part. The 'technique of feedback linearization and H-infinity control are used to design an adaptive control law for compensating the unknown nonlinearity part, such the effect of cogging torque as a disturbance will be decreased onto the angle and angular velocity tracking performances.
引用
收藏
页码：496 / 501
页数：6
相关论文
共 50 条
  • [21] Identification of the hydraulic AGC system of cold rolling mill using RBF neural network
    Chen, Huiyong
    He, Shanghong
    WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS, 2006, : 1383 - 1387
  • [22] Output feedback neural network adaptive robust control with anolication to linear motor drive system
    Gong, J. Q.
    Yao, Bin
    JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME, 2006, 128 (02): : 227 - 235
  • [23] Robust optimal control using recurrent dynamic neural network
    Karam, M
    Zohdy, MA
    Farinwata, SS
    PROCEEDINGS OF THE 2001 IEEE INTERNATIONAL SYMPOSIUM ON INTELLIGENT CONTROL (ISIC'01), 2001, : 331 - 336
  • [24] Adaptive Trajectory Neural Network Tracking Control for Industrial Robot Manipulators with Deadzone Robust Compensator
    La Van Truong
    Shou Dao Huang
    Vu Thi Yen
    Pham Van Cuong
    International Journal of Control, Automation and Systems, 2020, 18 : 2423 - 2434
  • [25] Adaptive Trajectory Neural Network Tracking Control for Industrial Robot Manipulators with Deadzone Robust Compensator
    La Van Truong
    Shou Dao Huang
    Vu Thi Yen
    Pham Van Cuong
    INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, 2020, 18 (09) : 2423 - 2434
  • [26] A Novel Intelligent Mobile Robot Navigation Technique for Avoiding Obstacles using RBF Neural Network
    Panigrahi, Pratap Kumar
    Ghosh, Saradindu
    Parhi, Dayal R.
    2014 INTERNATIONAL CONFERENCE ON CONTROL, INSTRUMENTATION, ENERGY & COMMUNICATION (CIEC), 2014, : 1 - 6
  • [27] Robust tracking control of uncertain dynamic nonholonomic systems using recurrent neural networks
    Miao, Zhiqang
    Wang, Yaonan
    Yang, Yimin
    NEUROCOMPUTING, 2014, 142 : 216 - 227
  • [28] Output-Tracking Quantized Explicit Nonlinear Model Predictive Control Using Multiclass Support Vector Machines
    Chakrabarty, Ankush
    Buzzard, Gregery T.
    Zak, Stanislaw H.
    IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2017, 64 (05) : 4130 - 4138
  • [29] RBF Neural Network-Based Adaptive Robust Synchronization Control of Dual Drive Gantry Stage With Rotational Coupling Dynamics
    Shi, Pengwei
    Sun, Weichao
    Yang, Xuebo
    IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2023, 20 (02) : 1059 - 1068
  • [30] Robust path tracking control of mobile robot via dynamic petri recurrent fuzzy neural network
    Wai, Rong-Jong
    Liu, Chia-Ming
    Lin, You-Wei
    SOFT COMPUTING, 2011, 15 (04) : 743 - 767
12345