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Model predictive control of induction motors based on improved discretizing method under low switching frequency

Traction system is the “heart” unit of railway vehicles, its performance is crucial to the improvement of vehicle performances. With the rapid development of controller chips, the high performance control algorithms of induction motors have been applied into the traction systems. Model predictive control has some advantages, such as simple principle, easy modeling, handling nonlinear constraints, and strong robustness. Therefore, it has been widely used in induction motor drive systems. However, the traction converters have some characteristics of low switching frequency and large control period. Due to the discretization error, the performances of model predictive control will be limited dramatically. In order to solve the problem, the improved Taylor method is introduced into the discretization of model predictive control algorithm in this paper. The simulation results show that the model predictive control discretized by the improved Taylor method has better performances than the one discretized by the Euler method. The proposed improved Taylor method can improve the accuracy of the speed response and the dynamic performance of the system effectively.