Adaptive Dynamic Programming-Based Feature Tracking Control for Image Disturbances Visual Servoing Manipulators

Aiming at the problem of feature point interference and noise in visual servoing (VS) control, an image feature tracking control method based on adaptive dynamic programming (ADP) is proposed. The output signal of the image sensor is used as the feedback signal of the VS manipulators system for closed-loop control. While the adaptive state observer is used to estimate the image disturbances in the VS model of the manipulator, and the cost function is improved according to the observed disturbance values. On the basis of ADP technology, a critic neural network (NN) was constructed to derive the Hamilton-Jacobi-Bellman (HJB) equation. According to Lyapunov theory, it is proved that the visual servoing manipulators system is asymptotically stable. Finally, numerical simulations are given to verify the effectiveness of the algorithm.