High performance motion control of DC motors using wavelet networks

Hasan A. Yousef; Mohamed E. Elkhatib; Omar A. Sebakhy

doi:10.1109/ACC.2008.4586865

High performance motion control of DC motors using wavelet networks

Hasan A. Yousef, Mohamed E. Elkhatib, Omar A. Sebakhy

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

Based on the multi-resolution analysis and the wavelet transform, a wavelet network is presented for the control of DC motors. One of the basic advantages of wavelet network is that training is done using the recursive least square method which is suitable for online training usually required for adaptive control. The wavelet network is used to design adaptive speed controllers for a DC motor to achieve high performance speed control even if the motor model is unknown, the load characteristics are also unknown function of speed and the load torque changes online. Simulation and experimental results are presented to validate the proposed controllers.

Original language	English
Title of host publication	Proceedings of the American Control Conference
Pages	2492-2497
Number of pages	6
DOIs	https://doi.org/10.1109/ACC.2008.4586865
Publication status	Published - 2008
Event	2008 American Control Conference, ACC - Seattle, WA, United States Duration: Jun 11 2008 → Jun 13 2008

Other

Other	2008 American Control Conference, ACC
Country	United States
City	Seattle, WA
Period	6/11/08 → 6/13/08

ASJC Scopus subject areas

Electrical and Electronic Engineering

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10.1109/ACC.2008.4586865

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title = "High performance motion control of DC motors using wavelet networks",

abstract = "Based on the multi-resolution analysis and the wavelet transform, a wavelet network is presented for the control of DC motors. One of the basic advantages of wavelet network is that training is done using the recursive least square method which is suitable for online training usually required for adaptive control. The wavelet network is used to design adaptive speed controllers for a DC motor to achieve high performance speed control even if the motor model is unknown, the load characteristics are also unknown function of speed and the load torque changes online. Simulation and experimental results are presented to validate the proposed controllers.",

author = "Yousef, {Hasan A.} and Elkhatib, {Mohamed E.} and Sebakhy, {Omar A.}",

year = "2008",

doi = "10.1109/ACC.2008.4586865",

language = "English",

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AU - Elkhatib, Mohamed E.

AU - Sebakhy, Omar A.

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N2 - Based on the multi-resolution analysis and the wavelet transform, a wavelet network is presented for the control of DC motors. One of the basic advantages of wavelet network is that training is done using the recursive least square method which is suitable for online training usually required for adaptive control. The wavelet network is used to design adaptive speed controllers for a DC motor to achieve high performance speed control even if the motor model is unknown, the load characteristics are also unknown function of speed and the load torque changes online. Simulation and experimental results are presented to validate the proposed controllers.

AB - Based on the multi-resolution analysis and the wavelet transform, a wavelet network is presented for the control of DC motors. One of the basic advantages of wavelet network is that training is done using the recursive least square method which is suitable for online training usually required for adaptive control. The wavelet network is used to design adaptive speed controllers for a DC motor to achieve high performance speed control even if the motor model is unknown, the load characteristics are also unknown function of speed and the load torque changes online. Simulation and experimental results are presented to validate the proposed controllers.

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BT - Proceedings of the American Control Conference

T2 - 2008 American Control Conference, ACC

Y2 - 11 June 2008 through 13 June 2008

ER -

High performance motion control of DC motors using wavelet networks

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