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Hybrid Neuro-Fuzzy Network Identification for Autonomous Underwater Vehicles

  • Osama Hassanein
  • G. Sreenatha
  • Tapabrata Ray
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8298)

Abstract

Autonomous Underwater Vehicles (AUVs) are ideal platforms for aquatic search and rescue operations and exploration. The AUV poses serious challenges due to its complex, inherently nonlinear and time-varying dynamics. In addition, its hydrodynamic coefficients are difficult to model accurately because of their variations under different navigational conditions and manoeuvring in uncertain environments. This paper introduces an identifier scheme for identification of non-linear systems with disturbances based on Hybrid Neuro-Fuzzy Network (HNFN) technique. The method comprises of an automatic structure-generating phase using entropy based technique. The accuracy of the model is suitably controlled using the entropy measure. To improve the accuracy and also for generalization of the model to handle different data sets, Differential Evolution technique (DE) is employed. Finally, Hardware In-Loop (HIL) simulation and real-time experiments using the proposed algorithm to identify the 6-DOF UNSW Canberra AUV’s dynamics are implemented. The modelling performance and generalisation capability are seen to be superior with our method.

Keywords

Membership Function Differential Evolution Fuzzy Rule Underwater Vehicle Autonomous Underwater Vehicle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Osama Hassanein
    • 1
  • G. Sreenatha
    • 1
  • Tapabrata Ray
    • 1
  1. 1.School of Engineering and Information TechnologyUNSW@ADFACanberraAustralia

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