The Research Of Control System In Industry Sewing Machine Based On BP Neural Network

China is a country with prosperous sew industry, and also the main a manufacture base of industrial sewing machine in the world. Nowadays the control of industry sewing machine is based on DSP, and this system is usually integrated with optical, electrical and magnetic as the essential monitoring and controlling technology. In order to meet the sewing machine performance requirements, which is fast starting and stopping, wide range of speed changing and stopping at a precise position. Having experienced revision and commissioning many times, it has basically meet the demand of massive industrial production. The main work of this paper is as follows.(1) Based on the introduction of structure and mathematical model of BLDCM, the paper comes up with speed control strategy accordingly. Because industry sewing machine system is hysteretic, time-variant and nonlinear, BP neural algorithm is added to the conventional PID control algorithm, whose self-learning and self-adjusting capabilities help realize the thoughts that parameters of a PID controller can regulate themselves as the system is varying, and this thought will also be emulated by MATLAB and used in experimental coding.(2) The hardware and software for Industrial Sewing Machine is designed, including control circuit of DSP and peripheral circuit, power circuit, IPM driving circuit, pedal circuit. Because the Direct-Drive Industrial Sewing Machine has complex logic and functions, the software makes the program as modules.(3) Testing the system's control performance and stopping position, which demonstrates that the control performance of BP-PID is more super than that of conventional PID. What's more, by testing other performances of the designed control system, the results all meet the practical using need.