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Experimental study on a learning control system with bound estimation for underwater robots

Experimental study on a learning control system with bound estimation for underwater robots Underwater robotic vehicles (URVs) have become an important tool for numerous underwater tasks due to their greater speed, endurance, depth capability, and a higher factor of safety than human divers. However, most vehicle control system designs are based on simplified vehicle models and often result in poor vehicle performance due to the nonlinear and time-varying vehicle dynamics having parameter uncertainties. Conventional proportional-integral-derivative (PID) type controllers cannot provide good performance without fine-tuning the controller gains and may fail for sudden changes in the vehicle dynamics and its environment. Conventional adaptive control systems based on parameter adaptation techniques also fail in the presence of unmodeled dynamics. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Autonomous Robots Springer Journals

Experimental study on a learning control system with bound estimation for underwater robots

Autonomous Robots , Volume 3 (3) – Jun 9, 2004

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References (6)

Publisher
Springer Journals
Copyright
Copyright
Subject
Engineering; Robotics and Automation; Artificial Intelligence; Computer Imaging, Vision, Pattern Recognition and Graphics; Control, Robotics, Mechatronics
ISSN
0929-5593
eISSN
1573-7527
DOI
10.1007/BF00141154
Publisher site
See Article on Publisher Site

Abstract

Underwater robotic vehicles (URVs) have become an important tool for numerous underwater tasks due to their greater speed, endurance, depth capability, and a higher factor of safety than human divers. However, most vehicle control system designs are based on simplified vehicle models and often result in poor vehicle performance due to the nonlinear and time-varying vehicle dynamics having parameter uncertainties. Conventional proportional-integral-derivative (PID) type controllers cannot provide good performance without fine-tuning the controller gains and may fail for sudden changes in the vehicle dynamics and its environment. Conventional adaptive control systems based on parameter adaptation techniques also fail in the presence of unmodeled dynamics.

Journal

Autonomous RobotsSpringer Journals

Published: Jun 9, 2004

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