基于级联模型参考自适应的永磁同步电机模型预测控制OA

To address the issues of large current fluctuations and susceptibility to system parameter variations in traditional limited-set model predictive control strategies for permanent magnet synchro-nous motor(PMSM),a dual-vector model predictive control strategy based on a novel model reference adaptive parameter identification was proposed.Based on the mathematical model of the PMSM,the in-fluence of motor parameter variations on the system was thoroughly analyzed,and an error model of the dual-vector model predictive system under parameter mismatch was established.The traditional adap-tive algorithm was improved to form a cascaded model reference adaptive parameter identification meth-od,which could achieve online identification of the motor's resistance,inductance,and flux linkage and enabling real-time correction and compensation of system errors.The model was simulated in a MATLAB/Simulink environment.The results show that the proposed control strategy can respond to mo-tor parameter variations within 0.012 s,with parameter identification errors within a reasonable range,validating the robustness of the strategy.