Model-Free Predictive Current Control for a New Neutral Point Connected Open-End Winding Induction Motor Based on an Improved Sliding Mode Observer

Conventional ultra local model (ULM) based model-free predictive current control (MFPCC) for open-end winding induction motor (OEWIM) employs an extended state observer (ESO) to observe the disturbance, which significantly reduces sensitivity to certain motor parameters. However, the conventional ESO has poor disturbance steady state observation performance, which is also sensitive to high-frequency noise. To solve these problems, this paper proposes a novel model-free predictive current control method for a new neutral point connected OEWIM, combined with an improved sliding mode observer (SMO) to enhance the disturbance observation accuracy. Firstly, a calculation complexity reduced model predictive control method for OEWIM is proposed by designing an improved optimal voltage vector selection method. Secondly, the limitations of conventional ESO based MFPCC are analyzed through transfer function analysis. Thirdly, an SMO with dual low pass filters (DLPFs) is designed to estimate the unknown disturbance and compensate its amplitude and phase errors. Due to the designed compensation algorithm based on DLPFs, the disturbance can be observed accurately with reduced sensitivity to high-frequency noise. Thereby, the current control performance of the OEWIM is improved. Experimental results show that compared with traditional ESO based MPFC methods, the proposed approach demonstrates superior performance in terms of current harmonics, tracking error, and dynamic overshoot.