Research On Permanent Magnet Planar Motor Control System Based On Digital Signal Processor

With the development of digital control technology and improving of planar motor in the industry, traditional motion system which used single-chip in planar motors equipment already couldn't be fit to new meets which were in the velocity and precision. That forced to mend it by using some new ways and new technologies.This dissertation expounded the motion system on digital signal processor in detail. The core of the motion system was servo-motion controller. This controller's core chip was TMS320LF2407A of TI Company and dealt with feedback signal through detection. The controller took over statement from the PC by SCI bus and reached to control current loop by PID and position loop through predictive control.This dissertation established mathematical model of a permanent magnet planar motor with features of motion control system,analyzed permanent magnet planar motor operating principle and proposed overall design of open-system. In the dissertation, hardware components of system were stressed.. The system circuit of system by types of chips were conformed, including D/A, A/D, communication between PC and DSP, circuit of expanded RAM and so on. It identified main circuit of the electrical power, and put forward the main protection circuit and brake circuit basis on characteristics of permanent magnet motor plane. Two types of sensors also were used to control object signal taking into account of requirements of the system. Hall sensor was used to detect the electrical three-phase current and dual-band laser interferometer was used for position signal acquisition of motor. In the part of software design, initialization procedures and interrupt procedures of the control system were programmed by C language and it was compiled by CCS software.Control algorithm was researched. Basis on the positional accuracy of the request of permanent magnet planar motor, predictive control algorithm was proposed. System output would be measured and forecasted model output feedback to the error through continuous online rolling optimization. To a certain extent, it overcome the prediction model error and did not uncertain of effects on interference and strengthen the robustness of the system. The simulation of predictive control algorithm through MATLAB software was proved to be a good dynamic characteristic.