Multi-Optimization Objective Online Tracking-Based Parameter Self-Tuning Method for Sensorless PMSM Drives

It is important to optimize control parameters for the high-performance operation of position sensorless permanent magnet synchronous motor (PMSM) drives. In this article, a multi-optimization objective online tracking (MOOT)-based parameter self-tuning method is proposed to improve the sensorless control performance when the high-frequency signal injection (HFSI) method is used, which involves both offline and online stages. By the parameter self-learning and the model analysis, the injection signal, the position observer, the speed, and current regulators are preliminarily designed offline to ensure that the system can start smoothly. Based on the parameter characteristics analysis, an adaptive law with the speed ripple and torque ripple as the optimization index is designed to realize the online tracking of control and observation parameters. To solve the issues of tuning delay and mistuning, a moving window-based error detection strategy is investigated, which improves the parameter self-tuning speed and accuracy. The proposed method can improve the steady- and transient-state performance and realize the sensorless debug-free operation for HFSI-based method. The effectiveness is verified by experiments on a 2.2-kW PMSM drive platform.