Presents a neuro-fuzzy controller for autonomous underwater vehicles (AUVs) of which the dynamics are highly nonlinear, coupled, and time-varying. Modified fuzzy membership function-based neural networks (FMFNN) are used to combine advantages of fuzzy logics and neural networks, such as inference capability and adoption of human operators' fuzzy logics, and universal learning capability with neural networks. With initial fuzzy rules given by a human operator or automatically generated by a controller, the AUV can learn appropriate control parameters without human intervention, taking into account the differences in sensor characteristics in different environments. Internal learning loops and simplified derivatives of unknown systems are used for fast and simple converging algorithms. Compared to other control methods, the proposed FMFNN control algorithm never requires any information on systems, off-line learning procedures, or human intervention to adjust parameters. To show the validity of the proposed algorithm, computer simulation results are presented.
['Tae Won Kim', 'Junku Yuh']
A novel neuro-fuzzy controller for autonomous underwater vehicles