Predictive Current Control of Permanent Magnet Synchronous Generators for More Electric Aircraft with Inductance Identification

Permanent magnet synchronous generators are widely used in more electric aircraft applications, yet their inductance parameters are susceptible to magnetic saturation effects, leading to reduced control stability. To address this issue, this paper proposes a closed-loop control strategy that integrates model reference adaptive identification with model predictive control. First, based on the mathematical model of the permanent magnet synchronous generator in the synchronous rotating reference frame, a predictive current control model is derived. Then, to tackle the time-varying nature of the inductance parameters, an online parameter identification model based on the model reference adaptive system is constructed. The adaptive law is designed using Popov's stability theory to achieve real-time identification of the motor inductance parameters. A simulation model comprising three core modules—the permanent magnet synchronous generator module, the model reference adaptive system module, and the predictive control module—was developed for validation. Simulation results demonstrate that the proposed strategy can accurately identify inductance parameters, significantly improve current control precision, and enhance system robustness. These findings validate the practicality and correctness of the method in controlling permanent magnet synchronous generators for more electric aircraft, providing both theoretical and simulation support for advancing predictive current control performance.