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Structural Engineering and Mechanics Volume 26, Number 4, July10 2007 , pages 377-392 DOI: https://doi.org/10.12989/sem.2007.26.4.377 |
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Sliding mode control based on neural network for the vibration reduction of flexible structures |
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Yong-an Huang, Zi-chen Deng and Wen-cheng Li
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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. | ||
| Key Words | ||
| sliding mode control; neural network; flexible structure; hybrid model. | ||
| Address | ||
| Yong-an Huang: School of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xi?an, 710072, China School of Mechanical Science & Engineering, Huazhong University of Science & Technology, Wuhan, 430074, China Zi-chen Deng: School of Mechanics, Civil Engineering and Architecture, Northwestern Polytechnical University, Xi?an, 710072, China State Key Laboratory of Structural Analysis of Industrial Equipment, Dalian University of Technology, Dalian, 116024, China Wen-cheng Li: School of Science, Northwestern Polytechnical University, Xi?an, 710072, China | ||