Model-Free Deadbeat Predictive Current Control for SPMSM Based on Adaptive Gain Extended State Observer
Xuan Wu, Jinyu Kang, Meizhou Yang, Ting Wu, Shoudao Huang
Abstract
The conventional model-free deadbeat predictive current control method with extended state observer (MFDPCC-ESO) has been a promising method because of its great dynamic performance and significant parameter robustness. However, the conventional ESO still depends on inductance for designing controller gain, which inevitably reduces the parameter robustness of the method. In order to address this issue, an MFDPCC method with adaptive gain ESO (AGESO) is proposed in this paper. First, the impact of the conventional ESO input gain on control performance was analyzed, and it points out that controller gain mismatch will increase current ripple and even cause system instability. Then, the proposed method is based on a modified ultralocal model, in which the disturbance only includes the inductance term. Using disturbance as the objective function and combining finite-time gradient method (FGD) to estimate inductance parameters adaptively, the effect of controller gain mismatch is suppressed. Finally, experimental research was conducted on a 1kW surface permanent magnet synchronous motor (SPMSM) test bench to verify the steady-state performance and parameter robustness of the method.