Research On Parameter Identification And Sensorless Control Of PMSM Based On Particle Swarm Algorithm

With the development of industrialization,the application of motors has become more extensive,and higher requirements for its control performance have been put forward.Permanent magnet synchronous motors(PMSM)occupy a large proportion in motor applications due to their high efficiency and simple structure.On the one hand,the PMSM control method is generally vector control,which is constructed by precise electrical and mechanical parameters.However,the stator resistance,inductance,and flux linkage will change with changes in factors such as the internal temperature and operating speed of the motor,resulting in a decrease in the stability and controllability of the motor.On the other hand,the vector control method needs to detect the rotor information of the motor.The traditional method is to add mechanical sensors,but there will be problems such as increased cost and increased motor volume,which is not suitable for application in special environments.Therefore,it is of great significance to the study of motor parameter identification methods and sensorless control.The main innovations of this article are as follows:(1)A hybrid particle swarm optimization algorithm(IMCPSO)with improved mutation crossover strategy is given.Aiming at the problem that the parameter identification of PMSM is a single objective,nonlinear,continuous optimization and needs to identify multiple parameters at the same time,the particle swarm optimization algorithm with the advantage of solving multi-objective is selected.In order to solve the problem that the independent adjustment of inertia weight and learning factor weakens the intelligence of PSO,first put forward the state factor to make the inertia weight adjust nonlinearly,and then the learning factor is expressed as a weighted logistic regression analysis function,so that it dynamically adjusts with the weight.To solve the loss of population diversity caused by the nonlinear model of the motor,the PSO introduced the cross-mutation strategy in the differential evolution algorithm produces candidate solutions,and the cross operators was used to improve the global search ability of the algorithm.The convergence of IMCPSO is proved mathematically.The optimization capabilities of the three algorithms of LPSO,SAPSO and IMCPSO are tested under four test functions to verify the effectiveness of IMCPSO from both theoretical and simulation aspects.(2)The mathematical model of PMSM parameter identification is constructed to realize the parameter identification based on IMCPSO.In order to solve the under-rank problem of the identification model,a negative sequence current with_di≠0 is injected instantaneously into the d-axis to establish a PMSM parameter identification mathematical model.Three kinds of working conditions,normal working condition,speed variation and torque variation,are designed.Without prior knowledge of motor design parameters,four parameters,stator resistance,dq axis inductance and permanent magnet flux linkage,are identified simultaneously The current loop controller is designed,and the parameter identification results are applied to the parameter tuning of the current loop controller to verify the dynamic performance of the motor.(3)A novel sensorless controller based on sliding mode observer is proposed.The traditional symbolic function is replaced by piecewise quadratic function,and the stability of the system is analyzed based on Lyapunov stability criterion The relationship between the sliding mode gain and the observed electromotive force is derived,and the variable sliding mode gain is given.The stator resistance identification link is added to the controller to further improve the stability of the system.The feasibility of the new controller is verified by experimental simulations under three working conditions:sudden changes in speed,sudden torque and changes in resistance.Finally,a PMSM system test platform based on STM32F405RGT6 control chip is built.With the help of power supply circuit,power drive module,three-phase full bridge inverter circuit and three resistance current sampling algorithm,the sensorless control verification of PMSM is realized through the combination of software and hardware design.