Research On Control Algorithm Optimization Of Permanent Magnet Synchronous Motor Considering Different Working Contidions

Permanent magnet synchronous motor(PMSM)has been widely used in motion control fields such as AC servo system and electric vehicle because of its good performance,wide speed regulation range and high reliability.The operating conditions of vehicle PMSM based on vector control are complex and changeable,the speed controller can not adjust adaptively,its parameters are difficult to adapt to the changes of working conditions,the stator current can not be decoupled dynamically,which affects the torque output performance,and then affects the overall control performance of electric vehicle.In this thesis,a speed adaptive control strategy based on radial basis function(RBF)neural network is proposed,which is aimed at improve the speed controller of PMSM vector control system can not adapt to the working conditions varied,resulting in the reduction of system dynamic performance,and further aiming at the problem that the d-q axis current can not be dynamically decoupled,which affects the motor torque output performance,a deviation decoupling control strategy based on parameter identification is proposed.The main work of this thesis as follows:1.Aiming at the problem of poor adaptive ability of traditional PID controller in vector control of PMSMr,an adaptive speed controller based on RBF neural network is proposed.Firstly,the thesis analyzes the working principle of traditional PID control and points out that PID controller cannot modify parameters adaptively due to the working conditions varied.Then,the learning algorithm of RBF neural network is deduced,and the optimal neural network structure is obtained through off-line training,and then the RBF-PID speed controller is established in Simulink simulation software.The Jacobian information of the control system is obtained online by neural network to correct the parameters of PID speed controller online,and the network parameters are updated through learning algorithm to obtain better control performance.Finally,a comparative simulation experiment is designed to verify the effectiveness and control performance of the RBF-PID speed controller proposed in this thesis.The simulation results show that compared with the traditional PID control,the RBF-PID algorithm possesses higher speed tracking accuracy and better dynamic performance.2.The d-q axis current of the PMSM under the vector control cannot be dynamically decoupled,lying difficulties in adapting to the dynamic changes of the motor parameters and then affecting the stability of the motor control system.Considering this situation,this thesis proposes a decoupling control strategy of the PMSM based on the parameter identification.First,taking the coupling coefficient of the d-q axis current controller as zero,and the transfer function of the deviation decoupling could be obtained under this condition.Then the phenomenon that the dynamic changes of the motor parameters such as the resistance,inductance and permanent magnet flux will affect the decoupling effect of the deviation decoupling can be observed.Thus,the recursive least square method with the forgetting factor is introduced to online identify the motor parameters,which are further used to correct the corresponding values in the deviation decoupling control model real time.On the basis,the current decoupling of d-q axis is realized,and the robustness of the control system to the motor parameter changes is improved.The deviation decoupling control model based on parameter identification is built in Simulink simulation software,and the decoupling effect and dynamic performance of the deviation decoupling method based on parameter identification are verified by simulation.3.In order to verify the feasibility and effectiveness of the speed adaptive control and current decoupling control strategies of PMSM proposed in this thesis in practical applications,an experimental platform of PMSM is established,which took TMS320F28335 as the main control chip and Infineon IKCM30F60 GA intelligent power module as the driver chip.The program of speed adaptive control and current deviation decoupling is designed and written in CCS development software.The experimental results of varied working conditions show that the speed adaptive control has good speed tracking accuracy and fast response ability,and the current decoupling has lower overshoot and smaller current amplitude fluctuation,which proves the effectiveness and feasibility of the proposed control strategy and good dynamic performance.The driving control model of electric vehicle based on PMSM was further built.The control algorithm proposed in this thesis is applied to the driving model,and the New Euroupean Driving Cycle(NEDC)is introduced for simulation verification.Low speed error and stable torque output prove that the proposed control strategy also has good control performance under complex working conditions.