Title :
Learning control for underwater robotic vehicles
Author_Institution :
Dept. of Mech. Eng., Hawaii Univ., Honolulu, HI, USA
fDate :
4/1/1994 12:00:00 AM
Abstract :
Underwater robotic vehicles 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 system designs have been based on a simplified vehicle model, which has often resulted in poor performance because the nonlinear and time-varying vehicle dynamics have parameter uncertainty. It was also observed by experiment that the thruster system had nonlinear behavior and its effect on vehicle motion was significant. It is desirable to have an advanced control system with the capability of learning and adapting to changes in the vehicle dynamics and parameters. This article describes a learning control system using neural networks for underwater robotic vehicles. Its effectiveness is investigated by simulation.<>
Keywords :
control system synthesis; intelligent control; learning (artificial intelligence); marine systems; mobile robots; neural nets; adaptive systems; dynamics; learning control system; mobile robots; neural networks; rigid body motion; thruster system; underwater robotic vehicles; Control system synthesis; Control systems; Humans; Nonlinear control systems; Nonlinear dynamical systems; Robot control; Time varying systems; Underwater vehicles; Vehicle dynamics; Vehicle safety;
Journal_Title :
Control Systems, IEEE