Research On Finite-control-set Model Predictive Current Control Of Permanent Magnet Synchronous Motor

Permanent magnet synchronous motor(PMSM)has the advantages of high efficiency and energy saving,diversified structure,high power factor,small volume and light weight.Permanent magnet synchronous traction system has broad development prospects in rail transit industry;Model predictive control(MPC)has become a popular alternative control strategy for PMSM because it is easy to restrict multi-objective,flexible optimization rules and easy to deal with nonlinear multivariable systems.However,PMSM model predictive current control(MPCC)also has some shortcomings,such as the mismatch of model parameters will increase the prediction deviation,multi-step prediction and multi-level prediction will sharply increase the computational complexity,and the multi-objective dimensions of cost function are inconsistent.The following research work focuses on these problems:(1)Based on the introduction of PMSM mathematical model and inverter switching vector,the principle of PMSM three vector voltage MPCC is analyzed.Its control links mainly include current prediction,switching vector calculation,voltage vector synthesis,cost function selection and so on;Then,it shows that the actual system has delay phenomena such as sampling feedback and switching action,so it is necessary to add delay compensation algorithm to MPCC;Finally,the MPCC strategy is realized on the MATLAB / Simulink simulation platform.(2)Aiming at the problem of system performance degradation caused by PMSM predictive current control model parameter mismatch,a control strategy based on internal model control observer(IMC)is proposed to deal with the mismatch problem.Firstly,according to the PMSM dynamic model in d-q coordinate system,the d-axis and q-axis current IMC is designed,the change rate of d-axis and q-axis current is estimated,and the motor parameter estimation is calculated online;Then,the optimal parameter estimation is obtained by reducing the system noise by Kalman filter,and the model parameters are corrected online after parameter thresholding;Finally,the proposed strategy is verified under two different mismatch conditions.(3)The traditional PMSM model predictive current control only optimizes in one sampling period,which is easy to fall into the local optimization problem,while the multi-step prediction increases the computational complexity exponentially.Therefore,a low computational complexity PMSM three-step predictive current control strategy is proposed.Firstly,after the delay compensation,the second step is the combination of three vector voltage control and duty cycle voltage control;Then,the third step prediction maintains the same voltage vector as the second step prediction;Finally,the optimal voltage vector is selected from the cost function.Compare the proposed strategy with other improvement strategies to verify its superiority.Aiming at the mismatch of multi-step predicted inductance parameters,the current robust control of d-q axis inductance double closed-loop structure is designed,and its effectiveness is verified under the condition of changing inductance model parameters.(4)Aiming at the multi-objective weight design problem of PMSM model predictive control,a new hybrid particle swarm optimization algorithm with low time cost is proposed to adjust the multi-objective weight.The fitness value of each particle and its derived particles is calculated only once in each iteration to reduce the iteration time cost of the algorithm.The particles are divided into excellent particles and non excellent particles.The excellent particles update the speed and position according to the particle swarm optimization algorithm,but the non excellent particles cross and mutate after updating the speed and position to produce new particles.The new particle position is replaced by the position corresponding to the individual extreme value with a certain probability.The proposed algorithm is compared with other algorithms to verify its superiority.(5)The cost function of the proposed PMSM multi-step predictive current control is designed,and a new hybrid particle swarm optimization algorithm is used to set the weight,observe the change of total harmonic distortion rate,and verify the effectiveness of the designed cost function.Under various working conditions of inductance parameter mismatch,the proposed current robust control strategy is adopted to observe the error of extracting inductance and verify its correctness and reliability.