Motion Controller for Autonomous Underwater Vehicle Based on Parallel Neural Network

This paper proposes a novel motion controller for autonomous underwater vehicle based on parallel neural network. The motion controller consists of a real-time part, a self-learning part and a desired state programming part, and it is different from normal adaptive neural network controller in structure. Owing to the introduction of the self-learning part, on-line learning can be performed without sample data in several sample periods, resulting in high learning speed of the controller and good control performance. The desired-state programmer is utilized to obtain better learning samples of the neural network to keep the stability of the controller. Finally, simulation is carried out on ZS-IV vehicle. The control performance is compared with that of neural network controller with different structures such as normal adaptive neural network. The results show that the AUV motion controller based on parallel neural network has a great possibility to solve the problems in the AUV control system design.