Open AccessComparison between current‐based and flux/torque‐based model predictive control methods for open‐end winding induction motor drives
Author(s) -
Zhu Bohang,
Rajashekara Kaushik,
Kubo Hajime
Publication year - 2017
Publication title -
iet electric power applications
Language(s) - English
Resource type - Journals
ISSN - 1751-8679
DOI - 10.1049/iet-epa.2016.0517
Subject(s) - control theory (sociology) , model predictive control , torque , direct torque control , torque ripple , induction motor , engineering , dynamometer , vector control , stall torque , computer science , voltage , automotive engineering , physics , control (management) , artificial intelligence , electrical engineering , thermodynamics
In automotive testing systems such as chassis dynamometers and engine dynamometers, effective and efficient control strategies are required to control the induction motor drives, so that fast torque response and low‐torque ripples can be obtained. The fast torque response can be obtained by using model predictive control (MPC) due to its high bandwidth over a wide‐speed range; and the torque ripple can be reduced by using open‐end winding induction motor (OEWIM). Since MPC can be current based or flux/torque based, and can be linear and non‐linear, it is necessary to evaluate the effectiveness of different MPC methods on the open‐end winding drives. In this study, linear and non‐linear current‐based MPC methods and flux/torque‐based MPC methods for OEWIM drive are derived and evaluated. The transient and steady‐state responses of MPC methods are compared through simulation and experiment. The results show that linear MPC methods require less computation time, and under the same sampling frequency, linear MPC methods provide lower current ripple and better zero‐sequence‐current suppression than non‐linear MPC methods. This study provides practical perception for MPC used on multi‐level converters and MPC used for unbalanced situations.