Composite learning adaptive sliding mode control for AUV target tracking

Yuyan Guo, Hongde Qin, Bin Xu, Yi Han, Quan Yong Fan, Pengchao Zhang

Research output: Contribution to journalArticlepeer-review

50 Scopus citations

Abstract

This paper studies the controller design for an autonomous underwater vehicle (AUV) with the target tracking task. Considering the uncertainty the nonlinear longitudinal model, a sliding mode controller is designed. Meanwhile the neural networks (NNs) are used to approximate the unknown nonlinear function in the model. To improve the NNs learning rapidity, the prediction error which reflect the learning performance is constructed, further the updating law is designed utilizing the composite learning technique. The system stability is guaranteed through the Lyapunov approach. The simulation results verify that the designed method could force the AUV to track the target until rendezvous, and the model uncertainty is addressed better via the composite learning algorithm.

Original languageEnglish
Pages (from-to)180-186
Number of pages7
JournalNeurocomputing
Volume351
DOIs
StatePublished - 25 Jul 2019

Keywords

  • Autonomous underwater vehicle
  • Composite learning
  • Neural networks
  • Sliding mode control
  • Target tracking

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