An Improved Model-Free Active Disturbance Rejection Deadbeat Predictive Current Control Method of PMSM Based on Data-Driven

Abstract

This article proposes an improved model-free active disturbance rejection deadbeat predictive current control(ADRDPCC) method for permanent magnet synchronous motor (PMSM) used in more electric aircraft (MEA) based on the data driven method, which is used to solve the parameter mismatch problem of deadbeat predictive current control (DPCC) and improve the performance of PMSM control system. DPCC model applied to the current loop of the MEA motor is established as the main control strategy of the system. The principle of active disturbance rejection control is combined with DPCC, and the ADRDPCC structure is formed to optimize the control strategy. A specific extended state observer (ESO) of ADRDPCC is designed to track the internal disturbance caused by parameter mismatch and the external disturbance in real time. DPCC is used as the control law of the ADRDPCC structure to predict the current and output the reference voltage based on the observation of ESO. A deep reinforcement learning model based on ADRDPCC is designed and trained based on the data-driven method. The trained model can compensate and optimize ADRDPCC based on the disturbance observed by ESO and the observed control state of PMSM. The simulated and experimental results show the superiority of the proposed method.