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Volume 28 Issue 3
 
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Article Navigation >  Journal of Unmanned Undersea Systems  > 2020  >  28(3): 284-290
WANG Xiang, ZHANG Yong-lin. Fractional-Order Sliding Mode Control Based on RBF Neural Network for AUV Path Tracking[J]. Journal of Unmanned Undersea Systems, 2020, 28(3): 284-290. doi: 10.11993/j.issn.2096-3920.2020.03.007
Citation: WANG Xiang, ZHANG Yong-lin. Fractional-Order Sliding Mode Control Based on RBF Neural Network for AUV Path Tracking[J]. Journal of Unmanned Undersea Systems, 2020, 28(3): 284-290. doi: 10.11993/j.issn.2096-3920.2020.03.007
shu

Fractional-Order Sliding Mode Control Based on RBF Neural Network for AUV Path Tracking

doi: 10.11993/j.issn.2096-3920.2020.03.007

  • School of Electronics and Information, Jiangsu University of Science and Technology, Zhenjiang 212003, China

  • Received Date: 2018-12-06
  • Rev Recd Date: 2019-01-04
  • Publish Date: 2020-06-30
    Abstract
  • Aiming at the path tracking problem of autonomous undersea vehicle(AUV) in the process of docking, a frac-tional-order sliding mode control algorithm based on radial basis function(RBF) neural network is proposed. Firstly, the AUV path tracking control algorithm is designed based on sliding mode control, the fractional calculus is introduced into the constant velocity reaching law of sliding mode control to alleviate the shaking of the system. Then, the RBF neural network is used to compensate for uncertainty in the AUV motion model and for external interference. Finally, the stability of the control system is proved by Lyapunov stability theory. Simulation results show that the designed controller can effectively track the path of AUV. Compared with the traditional sliding mode control and the fractional-order sliding mode control without considering system uncertainty and external interference, the proposed control method has faster tracking speed, higher stability and better tracking performance.

     

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