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 proceedingConference article published in proceeding or bookAcademic researchpeer-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 languageEnglish
Title of host publication2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages839-844
Number of pages6
ISBN (Electronic)9781509061815
DOIs
Publication statusPublished - 30 Jun 2017
Event2017 International Joint Conference on Neural Networks, IJCNN 2017 - Anchorage, United States
Duration: 14 May 201719 May 2017

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2017-May

Conference

Conference2017 International Joint Conference on Neural Networks, IJCNN 2017
CountryUnited States
CityAnchorage
Period14/05/1719/05/17

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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