Integration of Reference Current Slope Based Model-Free Predictive Control in Modulated PMSM Drives

Conventional model-based predictive current control (MBPCC) suffers from high current ripple and the system parameter dependence problem. To address these issues, a novel reference current slope based modulated model-free predictive control (MFPC) is proposed in this article. In the proposed method, a reference current slope is obtained based on the current deadbeat solution. A reduced amount of active voltage vectors can be then determined in a straightforward way according to the reference current slope and the current slopes for the possible voltage vectors, and thus avoiding the enumerating operation in the conventional MBPCC. To reduce the current ripples, a two-vector modulation strategy is introduced to the proposed method. In addition, the current slopes for the possible voltage vectors are obtained in a model-free manner based on online measured data only to reduce the influence of parameter uncertainties on the controller. Here, the principles of the two-vector modulation are well considered, and the current variations with the input voltage vectors from two sampling time intervals are used to estimate the current slopes for all the voltage vectors. As a result, even though two voltage vectors with variable time durations are applied in each control period, the current slope information can be refreshed every half control period, and thus guaranteeing the reliability and accuracy of current predictions. The proposed method is verified on a permanent magnet synchronous machine (PMSM) setup to demonstrate its improved parameter robustness and steady-state performance.