Adaptive backstepping control using recurrent neural network for linear induction motor drive

Faa Jeng Lin; Rong Jong Wai; Wen Der Chou; Shu Peng Hsu

doi:10.1109/41.982257

Adaptive backstepping control using recurrent neural network for linear induction motor drive

Faa Jeng Lin, Rong Jong Wai, Wen Der Chou, Shu Peng Hsu

Department of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

123 Scopus citations

Abstract

An adaptive backstepping control system using a recurrent neural network (RNN) is proposed to control the mover position of a linear induction motor (LIM) drive to compensate the uncertainties including the friction force in this paper. First, the dynamic model of an indirect field-oriented LIM drive is derived. Then, a backstepping approach is proposed to compensate the uncertainties including the friction force occurred in the motion control system. With the proposed backstepping control system, the mover position of the LIM drive possesses the advantages of good transient control performance and robustness to uncertainties for the tracking of periodic reference trajectories. Moreover, to further increase the robustness of the LIM drive, an RNN uncertainty observer is proposed to estimate the required lumped uncertainty in the backstepping control system. In addition, an online parameter training methodology, which is derived using the gradient-descent method, is proposed to increase the learning capability of the RNN. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results.

Original language	English
Pages (from-to)	134-146
Number of pages	13
Journal	IEEE Transactions on Industrial Electronics
Volume	49
Issue number	1
DOIs	https://doi.org/10.1109/41.982257
State	Published - Feb 2002

Keywords

Adaptive backstepping control
Field-oriented control
Linear induction motor
Lumped uncertainty
Recurrent neural network

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10.1109/41.982257

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title = "Adaptive backstepping control using recurrent neural network for linear induction motor drive",

abstract = "An adaptive backstepping control system using a recurrent neural network (RNN) is proposed to control the mover position of a linear induction motor (LIM) drive to compensate the uncertainties including the friction force in this paper. First, the dynamic model of an indirect field-oriented LIM drive is derived. Then, a backstepping approach is proposed to compensate the uncertainties including the friction force occurred in the motion control system. With the proposed backstepping control system, the mover position of the LIM drive possesses the advantages of good transient control performance and robustness to uncertainties for the tracking of periodic reference trajectories. Moreover, to further increase the robustness of the LIM drive, an RNN uncertainty observer is proposed to estimate the required lumped uncertainty in the backstepping control system. In addition, an online parameter training methodology, which is derived using the gradient-descent method, is proposed to increase the learning capability of the RNN. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results.",

keywords = "Adaptive backstepping control, Field-oriented control, Linear induction motor, Lumped uncertainty, Recurrent neural network",

author = "Lin, {Faa Jeng} and Wai, {Rong Jong} and Chou, {Wen Der} and Hsu, {Shu Peng}",

note = "Funding Information: Manuscript received November 24, 2000; revised July 9, 2001. Abstract published on the Internet December 5, 2001. This work was supported by the National Science Council of Taiwan, R.O.C., under Grant NSC 89–2213-E-033–048. F.-J. Lin is with the Department of Electrical Engineering, National Dong Hwa University, Hualien 974, Taiwan, R.O.C. R.-J. Wai is with the Department of Electrical Engineering, Yuan Ze University, Chung Li 320, Taiwan, R.O.C. W.-D. Chou is with the Department of Electrical Engineering, Chung Yuan Christian University, Chung Li 320, Taiwan, R.O.C. S.-P. Hsu was with the Department of Electrical Engineering, Chung Yuan Christian University, Chung Li 320, Taiwan, R.O.C. Publisher Item Identifier S 0278-0046(02)00932-2.",

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AU - Lin, Faa Jeng

AU - Wai, Rong Jong

AU - Chou, Wen Der

AU - Hsu, Shu Peng

N1 - Funding Information: Manuscript received November 24, 2000; revised July 9, 2001. Abstract published on the Internet December 5, 2001. This work was supported by the National Science Council of Taiwan, R.O.C., under Grant NSC 89–2213-E-033–048. F.-J. Lin is with the Department of Electrical Engineering, National Dong Hwa University, Hualien 974, Taiwan, R.O.C. R.-J. Wai is with the Department of Electrical Engineering, Yuan Ze University, Chung Li 320, Taiwan, R.O.C. W.-D. Chou is with the Department of Electrical Engineering, Chung Yuan Christian University, Chung Li 320, Taiwan, R.O.C. S.-P. Hsu was with the Department of Electrical Engineering, Chung Yuan Christian University, Chung Li 320, Taiwan, R.O.C. Publisher Item Identifier S 0278-0046(02)00932-2.

PY - 2002/2

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N2 - An adaptive backstepping control system using a recurrent neural network (RNN) is proposed to control the mover position of a linear induction motor (LIM) drive to compensate the uncertainties including the friction force in this paper. First, the dynamic model of an indirect field-oriented LIM drive is derived. Then, a backstepping approach is proposed to compensate the uncertainties including the friction force occurred in the motion control system. With the proposed backstepping control system, the mover position of the LIM drive possesses the advantages of good transient control performance and robustness to uncertainties for the tracking of periodic reference trajectories. Moreover, to further increase the robustness of the LIM drive, an RNN uncertainty observer is proposed to estimate the required lumped uncertainty in the backstepping control system. In addition, an online parameter training methodology, which is derived using the gradient-descent method, is proposed to increase the learning capability of the RNN. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results.

AB - An adaptive backstepping control system using a recurrent neural network (RNN) is proposed to control the mover position of a linear induction motor (LIM) drive to compensate the uncertainties including the friction force in this paper. First, the dynamic model of an indirect field-oriented LIM drive is derived. Then, a backstepping approach is proposed to compensate the uncertainties including the friction force occurred in the motion control system. With the proposed backstepping control system, the mover position of the LIM drive possesses the advantages of good transient control performance and robustness to uncertainties for the tracking of periodic reference trajectories. Moreover, to further increase the robustness of the LIM drive, an RNN uncertainty observer is proposed to estimate the required lumped uncertainty in the backstepping control system. In addition, an online parameter training methodology, which is derived using the gradient-descent method, is proposed to increase the learning capability of the RNN. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results.

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Adaptive backstepping control using recurrent neural network for linear induction motor drive

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