Current Deadbeat Control of Permanent Magnet-Assisted Synchronous Reluctance Motor Based on Parameter Identification
Xu Aide1, Liu Xin2, Li Xinyu2, Hu Shimai1
1. School of Information and Science Technology Dalian Maritime University Dalian 116026 China; 2. School of Electrical Engineering of Ships Dalian Maritime University Dalian 116026 China
Abstract：The permanent magnet-assisted synchronous reluctance motor, which combines the characteristics of permanent magnet synchronous motor and synchronous reluctance motor, has received more and more attention from many scholars because of its characteristics of less permanent magnet usage, high efficiency and high torque density. Deadbeat predictive control is widely used in modern motor control schemes. However, the control effect of deadbeat predictive control depends on the accuracy of the model, and in the actual operation of PMA-SynRM characterized by a high convex pole ratio, the motor parameters will be changed greatly with the change of operating conditions, which is more obvious in the motor inductance parameters. When the motor inductance parameter varies greatly, it leads to a decrease in the calculation accuracy of the current angle of the maximum torque per ampere controller and a decrease in the control effect of the deadbeat controller. Therefore, a parametric online identification method for the d and q axis inductance parameters of permanent magnet assisted synchronous reluctance motors is adopted in this paper. In order to improve the control effect of deadbeat predictive current control system, the identification value is used to replace the initial model inductance parameters in MTPA controller and deadbeat predictive current controller. First, this paper defines the position of the d and q axis in the rotor structure and derives a mathematical model of a permanent magnet-assisted synchronous reluctance motor. Secondly, according to the voltage equation of permanent magnet-assisted synchronous reluctance motor, the mathematical equation of traditional deadbeat predictive current control is deduced, and the parameter sensitivity of inductance parameters in traditional deadbeat predictive current control scheme is analyzed. According to the stability condition of closed-loop transfer function in discrete domain, the fluctuation range of d and q axis inductance which can keep the deadbeat predictive current controller stable is given. The controller is in an unstable state when the d and q axis inductance is greater than two times the nominal model inductance. Finally, the parameter adaptive rate of the model reference adaptive motor parameter identification system is designed according to Popov's super stability theory, and the inductance parameter online identification scheme of PMA-SynRM is given to complete the design of the variable parameter MTPA-DBPCC controller. In order to verify the effectiveness of the deadbeat predictive current control strategy based on parameter identification, the algorithm is simulated and verified in Matlab/Simulink environment, and the experiment is carried out on the experimental platform with TMS320F28335 controller as the core. The simulation and experimental results show that the deadbeat predictive current control scheme based on parameter identification can effectively suppress the system oscillation and current ripple caused by inductance parameter mismatch. When the d axis inductance is greater than two times the parameter mismatch, the peak values of d and q axis current fluctuations are reduced by about 142% and 55%, respectively; when the q axis inductance is greater than two times the parameter mismatch, the peak values of d and q axis current fluctuations are reduced by about 7.6% and 147%, respectively, thus the robustness of the system is improved.
许爱德, 刘鑫, 李新宇, 胡士迈. 基于参数辨识的永磁辅助同步磁阻电机电流无差拍控制[J]. 电工技术学报, 2024, 39(18): 5626-5638.
Xu Aide, Liu Xin, Li Xinyu, Hu Shimai. Current Deadbeat Control of Permanent Magnet-Assisted Synchronous Reluctance Motor Based on Parameter Identification. Transactions of China Electrotechnical Society, 2024, 39(18): 5626-5638.
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2024, Vol. 39 
