Intelligent integral backstepping sliding mode control using recurrent neural network for magnetic levitation system

Faa Jeng Lin, Syuan Yi Chen

研究成果: 書貢獻/報告類型會議貢獻

7 引文 斯高帕斯(Scopus)

摘要

An intelligent integral backstepping sliding mode control (IIBSMC) system using a multi-input multi-output (MIMO) recurrent neural network (RNN) is proposed to control the position of a levitated object of a magnetic levitation system considering the uncertainties in this study. First, the dynamic model of the magnetic levitation system is derived. Then, an integral backstepping sliding mode control (IBSMC) system with an integral action is proposed for the tracking of the reference trajectory. Moreover, to relax the requirements of the needed bounds and discard the switching function in IBSMC, an IIBSMC system using a MIMO RNN estimator is proposed to improve the control performance and further increase the robustness of the magnetic levitation system. The adaptive learning algorithms are derived using Lyapunov stability theorem to train the parameters of the RNN online. Finally, some experimental results of the tracking of periodic sinusoidal trajectory demonstrate the validity of the proposed IIBSMC system for practical applications.

原文英語
主出版物標題2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
DOIs
出版狀態已發佈 - 2010
對外發佈Yes
事件2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 - Barcelona, 西班牙
持續時間: 2010 七月 182010 七月 23

其他

其他2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
國家西班牙
城市Barcelona
期間2010/07/182010/07/23

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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