Research On Parameter Identification And Decoupling Adaptive Backstepping Control Of PMSM

Permanent magnet synchronous motor(PMSM)is an ideal driving motor for high efficiency and high precision motion control systems because of its advantages of high efficiency,wide speed range and simple structure.The design and implementation of high-performance PMSM control system is closely related to the motor parameters.However,the PMSM parameter values will change with the change of working conditions and operating environment,which will adversely affect the change of vector coordinates and the performance of the controller.In addition,the dynamic coupling relationship between current excitation and torque components in vector control also directly affects the dynamic performance of PMSM.To tackle the difficult issue,decoupling control method is applied for this case.But in many previous literatures,the traditional decoupling control method is constricted in the dependence of parameters,so it is extremely necessary to study the parameter identification of PMSM.Motivated by above discussions,parameter identification and decoupling control method based on parameter identification of PMSM will be investigated in this paper.The main research contents include:(1)Introduce the mathematical model of PMSM and analyze its parameter coupling,and deduce the state feedback decoupling control law.Additionally,the parameter sensitivity of vector control and decoupling control is analyzed,which provides theoretical analysis and technical support for subsequent research.(2)Aiming at the problems of low speed and low accuracy of PMSM multi-parameter identification,a parameter identification method based on the improved salp swarm algorithm (ISSA)is proposed.With consideration of the shortcomings of the original salp swarm algorithm(SSA),the adaptive evaluation of mobile strategies and the neighborhood optimal leading strategy are adopted to enhance the information exchange and collaboration among individuals.By introducing the reverse learning strategy to perturb the position of individuals,the possibility of parameter misconvergence is reduced.The simulation results demonstrate the advantages of the improved salp group algorithm in the speed and accuracy of parameter identification.(3)Based on the incremental estimation least square algorithm,combined with the forgetting factor and the coupling identification technology,a coupled incremental estimation least square identification algorithm is proposed.Compared with traditional incremental estimation least square identification algorithm,this algorithm avoids high-dimensional matrix inversion,and overcomes the problem of repeated calculation of subsystem parameter vectors by useing the subsystem coupling relationship.Therefore,this algorithm can minimize the calculation burden and improves the convergence performance of the identification algorithm.In addition,the forgetting factor can improve the real-time estimation ability of the algorithm to track the changes of PMSM parameters.Simulation results verify the effectiveness of the identification algorithm.(4)In order to overcome the influence of time-varying parameters of PMSM on decoupling control performance,a PMSM decoupling adaptive backstepping control strategy is proposed by combining the PMSM parameter online identification algorithm,state feedback decoupling and backstepping control technology.This strategy uses a coupled incremental estimation least square identification algorithm to identify PMSM parameters online,and the obtained parameters are fed back to the decoupled backstepping controller.Finally, the simulation and experiment verify that the strategy can identify the corresponding parameters accurately and realize PMSM decoupling control.