Observer-based adaptive neural network control for a class of remotely operated vehicles

In this paper, a new adaptive neural network control approach is developed for a class of remotely operated vehicles whose velocity state and angular velocity state in the body-fixed frame are unmeasured. Unlike most previous control approaches, it doesn't need thrust model and the thruster control signal is considered as the input of control system directly. Using local recurrent neural network to approximate the unknown nonlinear functions, an adaptive observer is introduced for state estimation. Under the framework of the backstepping design, adaptive neural network control law is constructed based on the output of local recurrent neural network and state estimation. The stability analysis is given by Lyapunov theorem. The effectiveness of the proposed control scheme is illustrated by simulations.