Structural Engineering and Mechanics

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Sliding mode control based on neural network for the vibration reduction of flexible structures

  • Huang, Yong-An (School of Mechanics, Civil Engineering and Architecture, Northwestem Polytechnical University, School of Mechanical Science & Engineering, Huazhong University of Science & Technology) ;
  • Deng, Zi-Chen (School of Mechanics, Civil Engineering and Architecture, Northwestem Polytechnical University, State Key Laboratory of Structural Analysis of Industrial Equipment, Dalian University of Technology) ;
  • Li, Wen-Cheng (School of Science, Northwestem Polytechnical University)
  • Received : 2006.03.29
  • Accepted : 2007.02.26
  • Published : 2007.07.10
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Abstract

A discrete sliding mode control (SMC) method based on hybrid model of neural network and nominal model is proposed to reduce the vibration of flexible structures, which is a robust active controller developed by using a sliding manifold approach. Since the thick boundary layer will reduce the virtue of SMC, the multilayer feed-forward neural network is adopted to model the uncertainty part. The neural network is trained by Levenberg-Marquardt backpropagation. The design objective of the sliding mode surface is based on the quadratic optimal cost function. In course of running, the input signal of SMC come from the hybrid model of the nominal model and the neural network. The simulation shows that the proposed control scheme is very effective for large uncertainty systems.

Keywords

References

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  1. Modeling and Computation for the High-Speed Rotating Flexible Structure vol.130, pp.4, 2008, https://doi.org/10.1115/1.2890386