Research On Multi-parameter Identification Method Of Permanent Magnet Synchronous Motor Based On Improved Grey Wolf Algorithm

Permanent Magnet Synchronous Motor(PMSM)has the characteristics of simple structure,fast response speed and high power density,and is more and more widely used in servo control,electric vehicles,industrial robots and other fields.The parameters of PMSM will change correspondingly in actual working conditions due to the effect of environmental factors.The fixed-parameter control mode cannot meet the high-performance control requirements of the motor.This paper focuses on multi-parameter identification of PMSM and vector control based on parameter identification in order to realize high-performance vector control of PMSM.1.In view of the shortcomings of difficult simultaneous identification of multiple parameters and low identification accuracy in the traditional parameter identification method of PMSM,a grey wolf optimization algorithm based on adaptive normal cloud model(CGWO)is proposed.The grey wolf population is updated and developed using the normal cloud model.The performance of the improved algorithm is evaluated using the benchmark function and then compared to the standard grey wolf optimization algorithm and two improved grey wolf optimization algorithms in PMSM parameter identification.A PMSM experimental platform is built to validate the validity of the improved algorithm in order to further validate the identification effect of the proposed improved algorithm.2.Aiming at the problem that grey wolf optimization algorithm is susceptible to local optimality when dealing with PMSM parameter identification,an improved grey wolf algorithm based on hybrid strategy(IHGWO)is proposed to identify PMSM parameters.In order to improve the probability of jumping out of the local optimum and improve the search accuracy of the algorithm,an improved sine cosine algorithm is introduced to update the position of the leading wolf,and Levy flight strategy is applied to update the optimal predation position of grey wolf.The performance of the improved algorithm is validated using the CEC2017 test function,and then compared to grey wolf optimization algorithm,particle swarm optimization algorithm,gravity search algorithm,salps swarm algorithm and two improved grey wolf optimization algorithms in PMSM parameter identification.For PMSM parameter identification,simulation and experiments show that the IHGWO algorithm has the best identification accuracy,convergence speed and stability.3.Considering that the current loop PI controller is easily affected by the changes of the resistance and inductance parameters during the motor operation and the coupling problem of the vector control current loop,first,a parameter tuning method of a current loop PI controller based on parameter identification is designed to improve vector control performance,and the effectiveness of this method in dealing with parameter changes is validated through simulation experiments.The d-q axis is then decoupled based on the parameter identification results and the current loop PI controller parameter settings.This method improves the motor system’s robustness and control performance.At the same time,it demonstrates the importance of parameter identification in improving the effect of vector control.