Adaptive neural network control of an underwater remotely operated vehicle (ROV)

  • Authors:

  • A. Bagheri;N. Amanifard;T. Karimi;M. H. Farahani;S. M. Besarati

  • Affiliations:

  • Dept. of Mechanical Engineering, Guilan University, Rasht, Iran;Dept. of Mechanical Engineering, Guilan University, Rasht, Iran;Dept. of Mechanical Engineering, Guilan University, Rasht, Iran;Dept. of Mechanical Engineering, Guilan University, Rasht, Iran;Dept. of Mechanical Engineering, Guilan University, Rasht, Iran

  • Venue:
  • ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
  • Year:
  • 2006

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Abstract

An adaptive neural network (NN) controller is developed for an underwater remotely operated vehicle (ROV). Radial basis neural network and multilayer neural network are used in the closed-loop to approximate the nonlinear dynamics of the vehicle. The low cost ROV was designed and fabricated by department Mechanical Engineering of Guilan University, is considered. This technique does not require the iterative off-line training process to identify the plant parameters. The stability of the presented control systems are guaranteed on the basis of the Lyapunov theory. Finally the performances of the vehicle with NN controllers are compared with a PD controller. The significant improvements are observed in tracking performance of the ROV in all controllable degrees of freedom.