Visual Servoing Control of Robotics With A Neural Network Estimator Based on Spectral Adaptive Law

In recent years, with the development of machine vision and other relative techniques, visual servoing control of robotics has been wildly applied. A complex calibration process is usually required to get the accurate parameters of the camera and the robot, so that getting the projection relationship between changes of images and movement of robot joints usually takes much effort. In order to solve this problem, a rectified linear unit (ReLU) activating neural network (NN) estimator is proposed to estimate the compound Jacobian matrix of the system in this article. The weight of the NN is updated online by a project algorithm with a novel spectral adaptive law which can effectively improve the generalization ability of the NN and the robustness of the system. By constructing a new Lyapunov function with the spectral norm of weight of NN, the stability of the proposed adaptive algorithm and the controller can be proved. Simulations and experimental results validate the effectiveness of the proposed controller.