A dynamic neural controller for adaptive optimal control of permanent magnet DC motors

Yinyan Zhang; Shuai Li; Xin Luo; Ming Sheng Shang

doi:10.1109/IJCNN.2017.7965939

A dynamic neural controller for adaptive optimal control of permanent magnet DC motors

Yinyan Zhang, Shuai Li, Xin Luo, Ming Sheng Shang

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

4 Citations (Scopus)

Abstract

The speed control of permanent magnet brushed (PMB) DC motors at low speeds is difficult due to the nonlinearity caused by various types of frictions. Under parameter uncertainty, the speed control becomes more difficult. In this paper, to handle the parameter uncertainty, we propose a dynamic neural network to adaptively reconstruct or learn the dynamics of PMB DC motors. Then, based on the parameters of the neural dynamic model, a near-optimal dynamic neural controller is designed and proposed for the speed control of PMB DC motors with frictions considered under parameter uncertainty. Simulations substantiate the efficacy of the proposed dynamic neural model and adaptive near-optimal controller for PMB DC motors with fully unknown parameters.

Original language	English
Title of host publication	2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	839-844
Number of pages	6
ISBN (Electronic)	9781509061815
DOIs	https://doi.org/10.1109/IJCNN.2017.7965939
Publication status	Published - 30 Jun 2017
Event	2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States Duration: 14 May 2017 → 19 May 2017

Publication series

Name	Proceedings of the International Joint Conference on Neural Networks
Volume	2017-May

Conference

Conference	2017 International Joint Conference on Neural Networks, IJCNN 2017
Country/Territory	United States
City	Anchorage
Period	14/05/17 → 19/05/17

ASJC Scopus subject areas

Software
Artificial Intelligence

More information

10.1109/IJCNN.2017.7965939

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Zhang, Y., Li, S., Luo, X., & Shang, M. S. (2017). A dynamic neural controller for adaptive optimal control of permanent magnet DC motors. In 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings (pp. 839-844). Article 7965939 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2017-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2017.7965939

@inproceedings{820d08df7a6f4ee89bf4dabceb41f8d2,

title = "A dynamic neural controller for adaptive optimal control of permanent magnet DC motors",

abstract = "The speed control of permanent magnet brushed (PMB) DC motors at low speeds is difficult due to the nonlinearity caused by various types of frictions. Under parameter uncertainty, the speed control becomes more difficult. In this paper, to handle the parameter uncertainty, we propose a dynamic neural network to adaptively reconstruct or learn the dynamics of PMB DC motors. Then, based on the parameters of the neural dynamic model, a near-optimal dynamic neural controller is designed and proposed for the speed control of PMB DC motors with frictions considered under parameter uncertainty. Simulations substantiate the efficacy of the proposed dynamic neural model and adaptive near-optimal controller for PMB DC motors with fully unknown parameters.",

author = "Yinyan Zhang and Shuai Li and Xin Luo and Shang, {Ming Sheng}",

year = "2017",

month = jun,

day = "30",

doi = "10.1109/IJCNN.2017.7965939",

language = "English",

series = "Proceedings of the International Joint Conference on Neural Networks",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "839--844",

booktitle = "2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings",

note = "2017 International Joint Conference on Neural Networks, IJCNN 2017 ; Conference date: 14-05-2017 Through 19-05-2017",

}

Zhang, Y, Li, S, Luo, X & Shang, MS 2017, A dynamic neural controller for adaptive optimal control of permanent magnet DC motors. in 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings., 7965939, Proceedings of the International Joint Conference on Neural Networks, vol. 2017-May, Institute of Electrical and Electronics Engineers Inc., pp. 839-844, 2017 International Joint Conference on Neural Networks, IJCNN 2017, Anchorage, United States, 14/05/17. https://doi.org/10.1109/IJCNN.2017.7965939

A dynamic neural controller for adaptive optimal control of permanent magnet DC motors. / Zhang, Yinyan; Li, Shuai; Luo, Xin et al.
2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 839-844 7965939 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2017-May).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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Y1 - 2017/6/30

N2 - The speed control of permanent magnet brushed (PMB) DC motors at low speeds is difficult due to the nonlinearity caused by various types of frictions. Under parameter uncertainty, the speed control becomes more difficult. In this paper, to handle the parameter uncertainty, we propose a dynamic neural network to adaptively reconstruct or learn the dynamics of PMB DC motors. Then, based on the parameters of the neural dynamic model, a near-optimal dynamic neural controller is designed and proposed for the speed control of PMB DC motors with frictions considered under parameter uncertainty. Simulations substantiate the efficacy of the proposed dynamic neural model and adaptive near-optimal controller for PMB DC motors with fully unknown parameters.

AB - The speed control of permanent magnet brushed (PMB) DC motors at low speeds is difficult due to the nonlinearity caused by various types of frictions. Under parameter uncertainty, the speed control becomes more difficult. In this paper, to handle the parameter uncertainty, we propose a dynamic neural network to adaptively reconstruct or learn the dynamics of PMB DC motors. Then, based on the parameters of the neural dynamic model, a near-optimal dynamic neural controller is designed and proposed for the speed control of PMB DC motors with frictions considered under parameter uncertainty. Simulations substantiate the efficacy of the proposed dynamic neural model and adaptive near-optimal controller for PMB DC motors with fully unknown parameters.

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Zhang Y, Li S, Luo X, Shang MS. A dynamic neural controller for adaptive optimal control of permanent magnet DC motors. In 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 839-844. 7965939. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN.2017.7965939

A dynamic neural controller for adaptive optimal control of permanent magnet DC motors

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