FAT-based adaptive visual servoing for robots with time varying uncertainties

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.