Abstract
Underwater robotic vehicles (URVs) have become an important tool for various underwater tasks because they have greater speed, endurance, and depth capability as well as a higher factor of safety than human divers. However, most vehicle control systems were designed based on a simplified vehicle model and they often resulted in poor performance because of the nonlinear, time-varying vehicle dynamics and parameter uncertainties. This paper describes a vehicle control system that has the capability of learning and adapting to changes in the vehicle dynamics and parameters. The control system was compared with conventional linear control system by simulation
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