High-Performance Model-Free Predictive Control for PMSM Drives with Current Sensor Faults

In the operation of motor driving systems, performances may be impacted by factors such as alterations in motor physical parameters and phase current sensor faults. Model-free predictive control (MFPC) has been widely applied in motor driving systems to resist the influences caused by unsuitable physical parameters. However, the seriously reduced current quality caused by phase current sensor faults has not been considered yet. To solve this problem, a low imbalance MFPC strategy is proposed in this paper, and applied to a permanent magnet synchronous motor (PMSM) under conditions of phase current sensor faults. A time-series model is built and online updated by the recursive least square (RLS) algorithm to describe the current operation state of the motor, and the imbalance degree caused by the current sensor fault is corrected by flat Tan-Sun (FTS) transform. Tested across various amplitudes and offsets, the effectiveness of the proposed method is confirmed through experimental validations. The results highlight its superiority in reducing imbalance and enhancing current quality, compared with the model-based predictive control (MBPC) and conventional MFPC strategies without FTS transform.