Adaptive Critic Control of Autonomous Underwater Vehicles Using Neural Networks

Abstract

An adaptive critic neural network-based tracking autopilot design for autonomous underwater vehicles (AUV) will be proposed in this paper. The adaptive critic learning scheme consists of an associative search network (ASN), which is implemented by the three-layer neural network to approximate nonlinear and complex functions of autonomous underwater vehicles, and an adaptive critic network (ACN) generating the reinforcement signal to tune the ASN. A proportional gain controller, an ASN and an adaptive robust element, which can eliminate the approximation errors and disturbances, constitute the control law. The ASN and ACN have the same input and hidden layers, and different hidden-to-output weights. The stability of the closed-loop system can be proved by Lyapunov theory. Traditional adaptive critic controllers learn through trial-and-error interactions, however, the proposed tuning algorithm can significantly shorten the learning time by on-line tuning weights of ASN and ACN. The adaptive critic neural network-based controller is simulated for the tracking control of the AUV in 6 degrees of freedom to demonstrate the effectiveness.