Neural-network-based robust control for steer-by-wire systems with uncertain dynamics

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• 作者：Hai Wang ; Zhengming Xu ; Manh Tuan Do ; Jinchuan Zheng…
• 关键词：Finite ; time convergence ; Radial basis function neural network ; Robustness ; Steer ; by ; wire
• 刊名：Neural Computing & Applications
• 出版年：2015
• 出版时间：October 2015
• 年：2015
• 卷：26
• 期：7
• 页码：1575-1586
• 全文大小：2,073 KB
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• 作者单位：Hai Wang (1) (2)
  Zhengming Xu (2)
  Manh Tuan Do (1)
  Jinchuan Zheng (1)
  Zhenwei Cao (1)
  Linsen Xie (3)
  
  1. Faculty of Science, Engineering and Technology, Swinburne University of Technology, Hawthorn, VIC, 3122, Australia
  2. Lishui CA Steer-by-Wire Technological Co. Ltd., Zhejiang, China
  3. School of Engineering, Lishui University, Zhejiang, China
  
• 刊物类别：Computer Science
• 刊物主题：Simulation and Modeling
  
• 出版者：Springer London
• ISSN：1433-3058

文摘

This study develops a neural-network-based robust control scheme for steer-by-wire systems with uncertain dynamics. The proposed control consists of a nominal control and a nonsingular terminal sliding mode compensator where a radial basis function neural network (RBFNN) is adopted to adaptively learn the uncertainty bound in the Lyapunov sense such that the effects of uncertainties can be effectively eliminated in the closed-loop system. Using the proposed neural control scheme, not only the robust steering performance against parameter variations and road disturbances is obtained, but also both the control gain and the control design complexity are greatly reduced due to the use of the RBFNN. Simulation results are demonstrated to verify the superior control performance of the proposed control scheme, in comparison with other control strategies. Keywords Finite-time convergence Radial basis function neural network Robustness Steer-by-wire