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

Faa Jeng Lin, Syuan Yi Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 - Barcelona, Spain
Duration: 2010 Jul 182010 Jul 23

Other

Other2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010
CountrySpain
CityBarcelona
Period10/7/1810/7/23

Fingerprint

Magnetic levitation
Backstepping
Recurrent neural networks
Sliding mode control
Control systems
Trajectories
Switching functions
Adaptive algorithms
Learning algorithms
Dynamic models

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Cite this

Lin, F. J., & Chen, S. Y. (2010). Intelligent integral backstepping sliding mode control using recurrent neural network for magnetic levitation system. In 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010 [5596898] https://doi.org/10.1109/IJCNN.2010.5596898

Intelligent integral backstepping sliding mode control using recurrent neural network for magnetic levitation system. / Lin, Faa Jeng; Chen, Syuan Yi.

2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010. 2010. 5596898.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Lin, FJ & Chen, SY 2010, Intelligent integral backstepping sliding mode control using recurrent neural network for magnetic levitation system. in 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010., 5596898, 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010, Barcelona, Spain, 10/7/18. https://doi.org/10.1109/IJCNN.2010.5596898
Lin FJ, Chen SY. Intelligent integral backstepping sliding mode control using recurrent neural network for magnetic levitation system. In 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010. 2010. 5596898 https://doi.org/10.1109/IJCNN.2010.5596898
Lin, Faa Jeng ; Chen, Syuan Yi. / Intelligent integral backstepping sliding mode control using recurrent neural network for magnetic levitation system. 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010. 2010.
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