Vector-Based Model Predictive Hysteresis Current Control for Asynchronous Motor

Abstract

Model predictive control, especially model predictive current control, has received a great deal of attention in the motor drive field in recent years, due to its ability to render fast dynamic response and to handle multiple variables, nonlinearities, and system constraints in an intuitive way. However, the conventional single-vector-based model predictive hysteresis current control brings about some problems, such as high sampling frequency and poor steady-state control performance. In this paper, a novel double-vector-based model predictive hysteresis current control (DV-MPHCC), which utilizes an arbitrary vector and a zero vector to form a voltage vector combination, is proposed. To verify the improved performance over a short predictive horizon, a series of comparisons are conducted between the two control algorithms by simulation and experiment. Both simulation and experiment results validate that the DV-MPHCC proposed has advanced steady-state control performance, and a lower sampling frequency.