A Study on Model Predictive Current Control Method for Permanent Magnet Synchronous Motors in Electric Aircraft

This chapter delves into the advanced control strategies for permanent magnet synchronous motors (PMSM) used in electric aircraft, focusing on the model predictive current control (MPCC) method. The study introduces the concept of duty cycle into traditional MPCC to boost the dynamic performance of PMSM, addressing issues like large torque pulsation and poor current response. To tackle the problem of model parameter mismatch, the authors integrate a parameter identification strategy based on an Adaline neural network and the least mean square (LMS) algorithm. The chapter provides a detailed explanation of the mathematical models and algorithms used, including the discretization of the PMSM model and the implementation of the duty cycle MPCC. Simulation results confirm that the proposed algorithm significantly enhances the motor's current response precision, even under varying motor parameters. The study concludes that the improved duty cycle MPCC, combined with parameter identification, effectively reduces the influence of parameter mismatches and improves the overall performance of PMSM in electric aircraft. This chapter offers valuable insights into the optimization of motor control systems, making it a crucial read for professionals seeking to advance the capabilities of electric aircraft.