UMLP-IFOCS: Using Multi-Layer Perceptron for Intelligent Field Oriented Control System

Recently, a motor vector control system has been a hot spot for robot control. Whether it is a UAV or a household robot, the accuracy of motor vector control is becoming increasingly important. The traditional motor magnetic field vector control system (FOC) has some limitations. In the small brushless motor control system, the non-encoder type position sensor has a specific nonlinearity. At the same time, in the FOC control system, the PI controller in the FOC is also difficult to solve the nonlinear problem of the control system. Artificial intelligence is developing rapidly, and we can solve nonlinear problems through neural networks. Therefore, this problem presents an intelligent motor magnetic field vector control system (IFOC) based on MLP. Firstly, we design a new position sensor structure. Then, after obtaining the data of the linear position sensor, we use MLP to solve the nonlinear problem of the sensor. With the precise position sensor, we can transform from the static coordinate system to the rotating coordinate system (The dp coordinate system) through the Clarke-Park transformation and then introduce MLP to replace the original PI controller. Finally, we built an experimental platform to verify the feasibility of the IFOC control system.