Adaptive Neural Network-based Synchronization Control for Dual-drive Servo System

In this paper, the neural network control is proposed for master-slave method of dual-drive servo system application. The architecture of control system includes traditional PID, back propagation neural network (BPNN) and radial basis function neural network (RBFNN). The BPNN can adjust three parameters of traditional PID automatically. The RBFNN approximation can determine the characteristics of servo system from given input and output sets. By combining PID, BPNN and RBFNN structure, the adaptive neural network-based method can achieve accurate control of nonlinear systems in synchronization for dual-drive servo. It is shown that the system performance of synchronization control including the speed output, the accuracy and the robustness works well with better dynamic and static characteristics.