Application Of Intelligent Control In The Robotic Visual Servo System

Robotic visual servo control is one of the hotspots in robot research field today. Non-linearity, strong coupling and uncertainty are the main factors of influence to the visual servoing performance. It has the important significance that to study the methods to reduce the impact. Image based visual servo control system is introduced in this paper, and for the complex process of calculating the image Jacobin matrix, the intelligent control method is used to study the mapping relationship between 2D image features and 3D robot space. The main research contents are as follows:First, since the neural network has the advantage of approximating arbitrary nonlinear function, this paper used the neural network control method to learn the mapping relation between image features and robot joint angles. The system of robot visual servo is established and the simulation results show the effectiveness of the proposed scheme.Second, because of the inherent defect of neural network, a genetic algorithm is adopted to optimize the neural network, and the visual controller based on the neural network optimized by genetic algorithm is designed for the visual servo system. The simulation results show that it can enhance the response speed of the system.Third, the paper combined the fuzzy control theory and the wavelet neural network control theory to design the visual controller based on fuzzy wavelet neural network. Moreover, the wavelet neural network was trained by the visual language control rules summarized by human experts. According to the rules of language, the initial values of wavelet neural network parameters can be defined more easily. The simulation results prove the validity of this method.