Title :
Visual servoing with velocity observer and neural compensation
Author :
Yu, Wen ; Li, XiaoOu
Author_Institution :
Departamento de Control Autom., ClNVESTAV-IPN, Mexico City, Mexico
Abstract :
The normal visual servoing of robot has two drawbacks: it needs joint velocity sensors, and cannot guarantee zero steady state error. We make two modifications to overcome these problems. Sliding-mode observer is applied to estimate the joint velocities, and a RBF neural network is used to compensate gravity and friction. Based on Lyapunov and input-to-state stability analysis, we prove the stability of visual servoing system with observer and RBF neural networks.
Keywords :
Lyapunov methods; image motion analysis; observers; radial basis function networks; robot vision; stability; variable structure systems; Lyapunov method; RBF neural network; input-to-state stability analysis; neural compensation; robot visual servoing; sliding-mode observer; velocity observer; velocity sensors; zero steady state error; Asymptotic stability; Friction; Gravity; Manipulators; Neural networks; Orbital robotics; PD control; Robotics and automation; Service robots; Visual servoing;
Conference_Titel :
Intelligent Control, 2004. Proceedings of the 2004 IEEE International Symposium on
Print_ISBN :
0-7803-8635-3
DOI :
10.1109/ISIC.2004.1387726
