Ensemble Optimization Based Weighting Factor-Less Predictive Torque Control for Induction Machines
Xie Haotian1, Wang Fengxiang2, Ke Dongliang2, Tang Ying1, Kennel Ralph1
1. Chair of Electrical Drive Systems and Power Electronics Technical University of Munich Munich 80333 Germany; 2. National Local Joint Engineering Research Center for Electrical Drives and Power Electronics Quanzhou Institute of Equipment Manufacturing Haixi Institutes Chinese Academy of Sciences Quanzhou 362200 China
Abstract As an emerging control strategy, predictive torque control (PTC) selects the optimal switching state by calculating the torque and flux tracking errors in the objective function, which is widely spread in the AC electrical drive system. Compared with traditional field orient control (FOC), PTC shows its superiority of fast dynamic response and low switching frequency. In order to unify the different terms in the objective function, parameter design is required to modify the weighting of torque and flux. However, the empirical weighting parameter is not easy to be fine-tuned according to different operating conditions. To solve the aforementioned issue, an ensemble optimization based weighting factor-less PTC is proposed for induction machines. The proposed method optimizes the tracking errors of torque and flux simultaneously. Thus, three switching states for optimal torque and flux are obtained, respectively. The optimal solution is subsequently achieved by the ensemble optimization mechanism. The effectiveness of the proposed method is verified by experimental results. Compared with the conventional PTC, the proposed method performs better at both steady state and transient state.
Xie Haotian,Wang Fengxiang,Ke Dongliang等. Ensemble Optimization Based Weighting Factor-Less Predictive Torque Control for Induction Machines[J]. Transactions of China Electrotechnical Society, 2022, 37(12): 2992-3003.
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2022, Vol. 37 
