Control Strategy Of Permanent Magnet Synchronous Motor In Wide Temperature Range Considering Model Parameter Uncertainties

To reduce the heat generation and optimize the system control performance,it is necessary to reduce the loss of interior permanent magnet synchronous motor(IPMSM)operating in high temperature environment.Considering that the control performance of the motor drive system is determined by current loop,the related control algorithms are improved in this thesis.The efficiency optimal control is introduced,including the given value optimization and the current control process optimization.In addition,under the operation condition of wide temperature range,the parameters of IPMSM are affected by uncertainties such as temperature and magnetic circuit saturation,which will change within a certain range,resulting in low robustness of the system.Online parameter identification is introduced to realize the optimal estimation,so as to correct the current loop control algorithm.For the proposed optimal control strategy,the theoretical analysis is given,and the simulation and experimental verification are completed.To optimize the given value of current loop,an improved maximum torque per ampere(MTPA)control algorithm is proposed in this thesis,which simplifies the control structure and speeds up the convergence speed of the algorithm.The proposed algorithm directly calculates the differential term of electromagnetic torque,which is taken as the decision criterion of MTPA state.The criterion is utilized to compensate the initially set reference value of d-axis current online to realize the tracking and maintenance of MTPA state.The proposed method eliminates signal injection and demodulation,which speeds up the convergence speed of the algorithm.The d-axis current compensation control solves the problem that the convergence speed of conventional algorithm is sensitive to load conditions,which improves the robustness.The effectiveness and superiority of the proposed method are verified by theoretical analysis,simulation and experimental results.Compared with the existing methods,the convergence speed of the proposed algorithm is faster under different loads and the amount of calculation is reduced.To realize the process optimization of current control and make the actual current track the given value quickly,dead-beat predictive current control(DPCC)shows significant advantages.The basic theory of DPCC algorithm is introduced.Then,considering the uncertainties of model parameters,an adaptive dead-beat current predictive control(ADPCC)algorithm is designed,and the parameter sensitivity analysis of the algorithm is given.According to the theoretical analysis,the designed ADPCC algorithm only depends on the inductance parameters,and eliminates the current control error in the case of parameter mismatch.Finally,the simulation and experimental results show that the current control error of ADPCC is close to zero in the case of parameter mismatch.Considering the uncertainties of model parameters and the similarity of motor parameters required by the MTPA and ADPCC algorithms,from the perspective of optimal estimation,an optimized control strategy is proposed in this thesis.The strategy utilizes on-line parameter identification method to correct the proposed control algorithms,which is suitable for IPMSM operating in wide temperature range.Firstly,the FFRLS algorithm is utilized to estimate the parameters of IPMSM.Then,the estimated values of the parameters are utilized to replace the nominal parameters in the proposed control algorithms.Finally,the effectiveness of the proposed optimal control strategy combined with parameter identification is verified by simulation and experiment.Compared with the current loop control performance before correction,the experimental results of parameter correction show that the current buffeting phenomenon of ADPCC caused by parameter mismatch is eliminated.Under different working conditions,the control error of MTPA current vector angle and the amplitude of stator current are reduced.