Neural-Tuned PID controller for Point-to-point (PTP) positioning system: model reference approach

Point-to-Point (PTP) motion control systems play an important role in industrial engineering applications such as advanced manufacturing systems, semiconductor manufacturing systems and robot systems. Until know,PID(proportionalintegral-derivative) controllers are still the most popular controller...

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Main Authors: Martono, Wahyudi, Ahmad, Wali, Myo, Min Htut
Format: Conference or Workshop Item
Language:English
Published: 2009
Subjects:
Online Access:http://irep.iium.edu.my/19111/1/Neural-tuned_PID_Controller_05069204.pdf
http://irep.iium.edu.my/19111/
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spelling my.iium.irep.191112017-06-21T04:58:18Z http://irep.iium.edu.my/19111/ Neural-Tuned PID controller for Point-to-point (PTP) positioning system: model reference approach Martono, Wahyudi Ahmad, Wali Myo, Min Htut TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery Point-to-Point (PTP) motion control systems play an important role in industrial engineering applications such as advanced manufacturing systems, semiconductor manufacturing systems and robot systems. Until know,PID(proportionalintegral-derivative) controllers are still the most popular controller used in industrial control systems including PTP motion control systems due to their simplicity and also satisfactory performances. However, since the PID controller is developed based on the linear control theory, the controller gives inconsistent performance for different condition due to system nonlinearities. In order to overcome this problem, Neural-tuned PID control using model reference adaptive control (MRAC) is proposed. By using EMRAN (Extended Minimal Resource Allocation Algorithm) to train the Radial Basis Funciton (RBF)Network, the PID controller can learn, adapt and change its parameters based on the condition of the controlled-objectin real-time. The effectiveness of the proposed method is evaluated experimentally in real time using an experimental rotary positioning system. The experimental results show that the proposed system is better than classical PID controller in terms of not only positioning performance but also robustness to inertia variations. 2009 Conference or Workshop Item REM application/pdf en http://irep.iium.edu.my/19111/1/Neural-tuned_PID_Controller_05069204.pdf Martono, Wahyudi and Ahmad, Wali and Myo, Min Htut (2009) Neural-Tuned PID controller for Point-to-point (PTP) positioning system: model reference approach. In: 5th International Colloquium on Signal Processing and Its Application, 6-8 Mar 2009, Kuala Lumpur.
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
spellingShingle TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
Martono, Wahyudi
Ahmad, Wali
Myo, Min Htut
Neural-Tuned PID controller for Point-to-point (PTP) positioning system: model reference approach
description Point-to-Point (PTP) motion control systems play an important role in industrial engineering applications such as advanced manufacturing systems, semiconductor manufacturing systems and robot systems. Until know,PID(proportionalintegral-derivative) controllers are still the most popular controller used in industrial control systems including PTP motion control systems due to their simplicity and also satisfactory performances. However, since the PID controller is developed based on the linear control theory, the controller gives inconsistent performance for different condition due to system nonlinearities. In order to overcome this problem, Neural-tuned PID control using model reference adaptive control (MRAC) is proposed. By using EMRAN (Extended Minimal Resource Allocation Algorithm) to train the Radial Basis Funciton (RBF)Network, the PID controller can learn, adapt and change its parameters based on the condition of the controlled-objectin real-time. The effectiveness of the proposed method is evaluated experimentally in real time using an experimental rotary positioning system. The experimental results show that the proposed system is better than classical PID controller in terms of not only positioning performance but also robustness to inertia variations.
format Conference or Workshop Item
author Martono, Wahyudi
Ahmad, Wali
Myo, Min Htut
author_facet Martono, Wahyudi
Ahmad, Wali
Myo, Min Htut
author_sort Martono, Wahyudi
title Neural-Tuned PID controller for Point-to-point (PTP) positioning system: model reference approach
title_short Neural-Tuned PID controller for Point-to-point (PTP) positioning system: model reference approach
title_full Neural-Tuned PID controller for Point-to-point (PTP) positioning system: model reference approach
title_fullStr Neural-Tuned PID controller for Point-to-point (PTP) positioning system: model reference approach
title_full_unstemmed Neural-Tuned PID controller for Point-to-point (PTP) positioning system: model reference approach
title_sort neural-tuned pid controller for point-to-point (ptp) positioning system: model reference approach
publishDate 2009
url http://irep.iium.edu.my/19111/1/Neural-tuned_PID_Controller_05069204.pdf
http://irep.iium.edu.my/19111/
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score 13.211869