基于鲁棒递推最小二乘法的SynRM参数在线辨识方法OA

[Objective]Aiming at the nonlinear distortion of d-q axis inductance caused by magnetic saturation effects in the operation of synchronous reluctance motor(SynRM)drive systems,this paper proposes an online inductance parameter identification strategy based on robust recursive least square(RRLS).[Methods]Firstly,the predicted voltage difference was calculated to construct a historical prediction residual sequence,and rolling optimization was performed during motor operation to effectively reduce steady-state estimation errors caused by random data.Secondly,the predicted standard deviation was used as a robust scale to construct a robust loss function,which enhanced the algorithm's ability to resist load disturbances without significantly increasing the computational burden.Then,an approximate equilibrium condition was combined with an adaptive mechanism with a variable forgetting factor for recursive estimation,and accurate parameter values were obtained through multiple iterations.Finally,a SynRM control and parameter identification system was built in Matlab/Simulink,and the RRLS algorithm was compared with the traditional variable forgetting factor recursive least square(VFFRLS)under different operating conditions.[Results]The simulation results showed that under no-load and load disturbance conditions,the proposed RRLS algorithm had lower identification errors.The steady-state error of the d-axis inductance was less than 0.5%,and the steady-state error of the q-axis inductance was less than 4%.During the dynamic process,the d-axis overshoot was reduced from 25 mH by the VFFRLS algorithm to 12 mH by the proposed RRLS algorithm,and the q-axis overshoot was reduced from 33 mH to 13 mH.[Conclusion]Compared with the traditional VFFRLS algorithm,the RRLS algorithm proposed in this paper achieves high steady-state identification accuracy,reduces overshoot during dynamic processes,and demonstrates excellent online identification performance under load disturbances,with high system robustness.