Field-programmable gate array-based intelligent dynamic sliding-mode control using recurrent wavelet neural network for linear ultrasonic motor

F. J. Lin, Y. C. Hung, S. Y. Chen

Research output: Contribution to journalArticle

20 Citations (Scopus)

Abstract

A field-programmable gate array (FPGA)-based intelligent dynamic sliding-mode control (IDSMC) using recurrent wavelet neural network (RWNN) estimator is proposed to control the mover position of a linear ultrasonic motor (LUSM) in this study. First, the structure and operating principles of the LUSM are introduced briefly. Then, the dynamics of LUSM mechanism with the introduction of a lumped uncertainty, which include the friction force, is derived. Since the dynamic characteristics and motor parameters of the LUSM are non-linear and time-varying, an IDSMC using RWNN estimator is designed to achieve robust control performance of the LUSM drive system. The RWNN estimator is employed to estimate the non-linear functions including the system parameters and external disturbance. Moreover, the adaptive learning algorithm trained the parameters of the RWNN online is derived using the Lyapunov stability theorem. Furthermore, an FPGA chip is adopted to implement the developed control and on-line learning algorithms for possible low-cost and high-performance industrial applications. The experimental results show that excellent positioning and tracking performance are achieved. In addition, the robustness to parameter variations and friction force can be obtained as well using the proposed control system.

Original languageEnglish
Pages (from-to)1511-1532
Number of pages22
JournalIET Control Theory and Applications
Volume4
Issue number9
DOIs
Publication statusPublished - 2010 Sep
Externally publishedYes

Fingerprint

Ultrasonic Motor
Linear Motor
Wavelet Neural Network
Dynamic Control
Recurrent Neural Networks
Sliding mode control
Sliding Mode Control
Field Programmable Gate Array
Field programmable gate arrays (FPGA)
Ultrasonics
Neural networks
Estimator
Learning Algorithm
Friction
Learning algorithms
Lyapunov Theorem
Adaptive Learning
Lyapunov Stability
Stability Theorem
Dynamic Characteristics

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Control and Optimization

Cite this

@article{213a2d0324aa4bac8a2976140ca94aef,
title = "Field-programmable gate array-based intelligent dynamic sliding-mode control using recurrent wavelet neural network for linear ultrasonic motor",
abstract = "A field-programmable gate array (FPGA)-based intelligent dynamic sliding-mode control (IDSMC) using recurrent wavelet neural network (RWNN) estimator is proposed to control the mover position of a linear ultrasonic motor (LUSM) in this study. First, the structure and operating principles of the LUSM are introduced briefly. Then, the dynamics of LUSM mechanism with the introduction of a lumped uncertainty, which include the friction force, is derived. Since the dynamic characteristics and motor parameters of the LUSM are non-linear and time-varying, an IDSMC using RWNN estimator is designed to achieve robust control performance of the LUSM drive system. The RWNN estimator is employed to estimate the non-linear functions including the system parameters and external disturbance. Moreover, the adaptive learning algorithm trained the parameters of the RWNN online is derived using the Lyapunov stability theorem. Furthermore, an FPGA chip is adopted to implement the developed control and on-line learning algorithms for possible low-cost and high-performance industrial applications. The experimental results show that excellent positioning and tracking performance are achieved. In addition, the robustness to parameter variations and friction force can be obtained as well using the proposed control system.",
author = "Lin, {F. J.} and Hung, {Y. C.} and Chen, {S. Y.}",
year = "2010",
month = "9",
doi = "10.1049/iet-cta.2009.0066",
language = "English",
volume = "4",
pages = "1511--1532",
journal = "IET Control Theory and Applications",
issn = "1751-8644",
publisher = "Institution of Engineering and Technology",
number = "9",

}

TY - JOUR

T1 - Field-programmable gate array-based intelligent dynamic sliding-mode control using recurrent wavelet neural network for linear ultrasonic motor

AU - Lin, F. J.

AU - Hung, Y. C.

AU - Chen, S. Y.

PY - 2010/9

Y1 - 2010/9

N2 - A field-programmable gate array (FPGA)-based intelligent dynamic sliding-mode control (IDSMC) using recurrent wavelet neural network (RWNN) estimator is proposed to control the mover position of a linear ultrasonic motor (LUSM) in this study. First, the structure and operating principles of the LUSM are introduced briefly. Then, the dynamics of LUSM mechanism with the introduction of a lumped uncertainty, which include the friction force, is derived. Since the dynamic characteristics and motor parameters of the LUSM are non-linear and time-varying, an IDSMC using RWNN estimator is designed to achieve robust control performance of the LUSM drive system. The RWNN estimator is employed to estimate the non-linear functions including the system parameters and external disturbance. Moreover, the adaptive learning algorithm trained the parameters of the RWNN online is derived using the Lyapunov stability theorem. Furthermore, an FPGA chip is adopted to implement the developed control and on-line learning algorithms for possible low-cost and high-performance industrial applications. The experimental results show that excellent positioning and tracking performance are achieved. In addition, the robustness to parameter variations and friction force can be obtained as well using the proposed control system.

AB - A field-programmable gate array (FPGA)-based intelligent dynamic sliding-mode control (IDSMC) using recurrent wavelet neural network (RWNN) estimator is proposed to control the mover position of a linear ultrasonic motor (LUSM) in this study. First, the structure and operating principles of the LUSM are introduced briefly. Then, the dynamics of LUSM mechanism with the introduction of a lumped uncertainty, which include the friction force, is derived. Since the dynamic characteristics and motor parameters of the LUSM are non-linear and time-varying, an IDSMC using RWNN estimator is designed to achieve robust control performance of the LUSM drive system. The RWNN estimator is employed to estimate the non-linear functions including the system parameters and external disturbance. Moreover, the adaptive learning algorithm trained the parameters of the RWNN online is derived using the Lyapunov stability theorem. Furthermore, an FPGA chip is adopted to implement the developed control and on-line learning algorithms for possible low-cost and high-performance industrial applications. The experimental results show that excellent positioning and tracking performance are achieved. In addition, the robustness to parameter variations and friction force can be obtained as well using the proposed control system.

UR - http://www.scopus.com/inward/record.url?scp=77956633363&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77956633363&partnerID=8YFLogxK

U2 - 10.1049/iet-cta.2009.0066

DO - 10.1049/iet-cta.2009.0066

M3 - Article

AN - SCOPUS:77956633363

VL - 4

SP - 1511

EP - 1532

JO - IET Control Theory and Applications

JF - IET Control Theory and Applications

SN - 1751-8644

IS - 9

ER -