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Mission Control of Autonomous Underwater Vehicle Using Computational Intelligence

Amin, Reza | 2010

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  1. Type of Document: M.Sc. Thesis
  2. Language: English
  3. Document No: 40744 (58)
  4. University: Sharif University of Technology, International Campus, Kish Island
  5. Department: Science and Engineering
  6. Advisor(s): Khayyat, Amir Ali Akbar; Ghaemi Osgouie, Kambiz
  7. Abstract:
  8. This thesis describes different neural networks models and an approximation based neural network controller for autonomous underwater vehicles (AUVs). The online multilayer perceptron neural networks (OMLPNN) have been designed to perform modeling of AUVs of which the dynamics are highly nonlinear and time varying. The online recurrent multilayer perceptron neural networks (ORMLPNN) have been additionally designed to generate a memory to pervious states and increase the performance of the modeling. The designed OMLPNN and ORMLPNN with the use of backpropagation learning algorithm have advantages and robustness to model the highly nonlinear functions. The proposed neural networks architecture has been designed to model the test bed AUV named NPS AUV II. Simulation results show the effectiveness of the OMLPNN and ORMLPNN to deal with modeling of AUVs as it has good capability to incorporate the dynamics of the system. Additionally an online approximation based neural network has been proposed to steer the AUVs in a way to eliminate the tracking error. A novel neural network inverse model has been used to identify the propeller’s shaft speed and control surface angles to develop the required forces and moments for AUV body. Simulation results show the robustness of the proposed controller when the neural network inverse model had been pretrained well.
  9. Keywords:
  10. Neural Network ; Modeling ; Control ; Autonomous Underwater Vehicle

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