On the adaptive control of robot manipulators
International Journal of Robotics Research
Stable Adaptive Neural Network Control
Stable Adaptive Neural Network Control
Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximation Techniques
Adaptive Tracking Control for Robots with Unknown Kinematic and Dynamic Properties
International Journal of Robotics Research
Image moments: a general and useful set of features for visual servoing
IEEE Transactions on Robotics
Vision-Based Adaptive Tracking Control of Uncertain Robot Manipulators
IEEE Transactions on Robotics
Uncalibrated visual servoing of robots using a depth-independent interaction matrix
IEEE Transactions on Robotics
Dynamic Visual Tracking for Manipulators Using an Uncalibrated Fixed Camera
IEEE Transactions on Robotics
Hi-index | 0.00 |
Most present adaptive control strategies for visual servoing of robots have assumed that the unknown camera parameters, kinematics and dynamics of visual servoing system should be linearly parameterized in the regressor matrix form. This is because the limitation of the traditional adaptive design in which the uncertainties should be time-invariant such that all time varying terms in the visual servoing system are collected inside the regressor matrix. However, derivation of the regressor matrix is tedious. In this paper, a FAT (function approximation technique) based adaptive controller is designed for visual servo robots without the need for the regressor matrix. A Lyapunov-like analysis is used to justify the closed-loop stability and boundedness of internal signals. Moreover, the upper bounds of tracking errors in the transient state are also derived. Computer simulation results are presented to demonstrate the usefulness of the proposed scheme.