Adaptive control of an autonomous underwater vehicle testbed using neural networks

The control of autonomous underwater vehicles has been a challenge to control engineers due to combined nonlinear nature of both the vehicle itself and the environment in which they operate. This paper presents an implementation research on the adaptive controller of an autonomous underwater vehicle testbed in which the controller architecture is made using multilayered neural networks. The problem considered is that of designing a controller for an autonomous underwater vehicle to provide directional control. A flux gate compass is used to measure the yaw angle and yaw rate. Directional control is performed by two thrusters in the horizontal plane. Weight adaptation of the neural network is achieved by minimizing an objective function that is weighted sum of tracking errors and control input rates. According to the experimental tests on various command trajectories, we show that when the learning process is kept active through the control operation, the neural network adapts to time-varying plant dynamics as well as disturbance upsets