TY - JFULL AU - Ahmad Forouzantabar and Babak Gholami and Mohammad Azadi PY - 2012/8/ TI - Adaptive Neural Network Control of Autonomous Underwater Vehicles T2 - International Journal of Electrical and Computer Engineering SP - 865 EP - 871 VL - 6 SN - 1307-6892 UR - https://publications.waset.org/pdf/6416 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 67, 2012 N2 - An adaptive neural network controller for autonomous underwater vehicles (AUVs) is presented in this paper. The AUV model is highly nonlinear because of many factors, such as hydrodynamic drag, damping, and lift forces, Coriolis and centripetal forces, gravity and buoyancy forces, as well as forces from thruster. In this regards, a nonlinear neural network is used to approximate the nonlinear uncertainties of AUV dynamics, thus overcoming some limitations of conventional controllers and ensure good performance. The uniform ultimate boundedness of AUV tracking errors and the stability of the proposed control system are guaranteed based on Lyapunov theory. Numerical simulation studies for motion control of an AUV are performed to demonstrate the effectiveness of the proposed controller. ER -