A Novel Active Disturbance Rejection Control of PMSM Based on Deep Reinforcement Learning for More Electric Aircraft

Abstract

In this article, a novel active disturbance rejection control (ADRC) based on deep reinforcement learning (DRL) is proposed to improve the performance of permanent magnet synchronous motor (PMSM) for more electric aircraft (MEA). MEA motors have the requirements of safety and stability so that a new ADRC method is put forward based on the limitation of nonlinear error attenuation function of traditional ADRC. The flux weakening control model of PMSM is firstly established. Then the ADRC model is built and applied to the speed loop of the control system. In order to reduce the number of control parameters and the jitter of the control law, deep neural network is employed to replace the traditional control law. Markov decision process is integrated into the novel ADRC to establish DRL model. A method based on twin delayed deep deterministic (TD3) policy gradient algorithm is proposed to train the neural network and optimize the DRL model. Model predictive control and traditional ADRC are used as comparison algorithms. Simulation and experiments show the effectiveness and superiority of the proposed method.