Robust Model Predictive Current Control of SPMSM Based on a Newly Designed Adaptive Switching Hybrid Cost Function

The control effectiveness of model predictive current control (MPCC) for surface mounted permanent magnet synchronous motor (SPMSM) usually deteriorates with parameter mismatch and model uncertainties under different operation conditions, which lead to large current ripples, current tracking errors, and dynamic response degradation. To obtain excellent steady-state and dynamic performance, a robust MPCC based on a newly designed adaptive switching hybrid cost function is proposed. The adaptive switching hybrid cost function combines the merits of linear extended state observer (LESO) and improved prediction error correction (IPEC) strategies. A LESO with lower bandwidth is designed to be implemented in the steady-state to reduce the tracking errors and ripples of the current. To obtain excellent dynamic response and reduce the current tracking errors, an IPEC with a proportional-integral structure is proposed, integrating the accumulated error into the prediction error correction (PEC) strategy. In addition, an adaptive switching function (ASF) with a sliding window is designed to identify the current state and maintain a smooth switch between the LESO and IPEC. Hence, the proposed method inherits the excellent dynamic response of IPEC and the superior steady-state performance of LESO through the ASF. Finally, the control performance of the proposed method is evaluated by the experiment of parameters mismatch and switching methods.