The Research And Design Of QSP-2 Surface Mounting Machine Position Control System

This thesis mainly study the application of the fuzzy neural network based on the genetic algorithm to the position control system of QSP-2 surface mounting machine. Simulation and experimental results illustrate the excellent performance of the fuzzy neural network and the powerful advantage of the genetic algorithm in the multi-objective optimization.Firstly, BLDC structure, working principle and running characteristics are introduced in details. Then the mathematical model of BLDC is given. Based on it, the current control loop and the speed control loop are designed. By the experiment result it is proved that the current control loop has the strong capability of disturbance-rejection, speeding up the dynamic response process and the restriction of armature overloaded current and the speed control loop has the strong capability of disturbance-rejection and no control static error.Secondly, the position control loop is designed by the fuzzy neural network based on the genetic algorithm. The structure of the fuzzy neural network, the optimization process of the network structure and the membership function by the genetic algorithm are detailed to introduce by an instance. By the simulation results it is explicitly shown the excellent performance of the fuzzy neural network and the powerful advantage of the genetic algorithm in the multi-objective optimization.Finally, the realization of the position control system of QSP-2 based on the B30A8 and GT-400-SV-PCI-G is given.