Motion control of underwater vehicles based on robust neural network

Aiming at low response speed and sensitization to external disturbance in motion control of underwater vehicles by adopting neural network, a stable robust learning algorithm was presented based on variable structure control theory and error back propagation algorithm, and the global stability conditions were discussed in detail. Finally, simulation experiments were carried out on General Detection Remotely Operated Vehicle. The results show that it has good robustness to external noises and changing of learning-ratio, which reduces the abrasion of the mechanically-driven system greatly. It keeps learning of neural network fast and stable, which meets the requirement of realtime control and has theoretical and practical value.