Self-Tuning PI Controller for Permanent Magnet Synchronous Motor Based on Iterative Learning Control

Abstract

In order to deal with the uncertainties of permanent magnet synchronous servo motor, inertia, torque load and viscous damping coefficient are online identified based on recursive least-square (RLS) estimator simultaneously. Then, a novel self-tuning PI controller based on iteration self-learning scheme is designed. Along with the regulation process, the system can remember effective historical control experience so as to change the learning gain and speed up the time of the whole process. Once the expected output is reached, the system will log the current load torque and PI gain. A database of the matched parameter is also built. With the increasing time of tuning, the database information becomes rich. If the identified inertia, load torque and viscous damping coefficient match the database information, an ideal PI gain can be acquired immediately without the adjustment process. Result of simulations show that the self-tuning PI controller outperforms the fixed PI scheme in rise time, overshoot and precise of the output when the parameter varied.