Nonparametric Predictive Current Control for SPMSM With Adaptive Cascade Extended Noise State Observer

Conventional model-free predictive current control with linear extended state observer has been widely used in motor driving systems due to its straightforward implementation and strong robustness. However, its accuracy still suffers from noise and relies on inductance to design the input gain, which restrains high-performance development. To address these issues, a nonparametric predictive current control (NPCC) is proposed for surface-mounted permanent magnet synchronous motors. First, an extended noise state observer (ENSO) with an auxiliary state is presented to reduce the noise sensitivities. Then, a novel cascade ENSO (CENSO) introducing the cascaded structure is presented to further improve disturbance estimation capability. In this way, the proposed CENSO achieves comprehensive improvements in noise suppression and disturbance rejection. Moreover, to eliminate the influence of the inductance parameter mismatch, an adaptive control algorithm is designed to estimate the input gain online, thereby eliminating errors caused by inductance parameter mismatch and reducing current harmonics. Finally, through experimental comparison, it has been proven that NPCC has superior steady-state performance and robustness against noise and parameter variations.