Research On Parameter Identification And Decoupling Control Of Permanent Magnet Synchronous Moto

Permanent magnet synchronous motor(PMSM),with its advantages of high performance,high efficiency and high power density,is widely used in new energy vehicles,intelligent manufacturing,aerospace and other motion control systems under the environment of pursuing low-carbon and energy-saving emission reduction in today’s society.The high precision PMSM control system is not only related to the control method,but also has a great relationship with the electrical parameters of PMSM itself.However,affected by the actual working condition of PMSM,the parameters of PMSM will be changed,which will have adverse consequences on the control of PMSM.In addition,since the PMSM vector control decomposes the current into two components in the d-q axis,the dynamic coupling relationship between these two components also directly affects the high performance control of the PMSM,so the decoupling control technology needs to be used to decouple the PMSM control system to solve this problem.In this paper,to improve the performance of PMSM control system,the research is carried out in the direction of parameter identification as well as decoupling control techniques,and the main research contents are as follows.First,in order to overcome the problems that the algorithm is easy to fall into local optimum,poor recognition accuracy and too many iterations in the late iteration when the metaheuristic algorithm solves the PMSM parameter identification problem,a cross-variance strategy incorporating the differential evolutionary algorithm and a sparrow search algorithm incorporating dynamic search are proposed.The algorithm introduces cross-variation strategy and dynamic search on the basis of the original sparrow search algorithm,so that the algorithm can increase the diversity of populations in the early stage and avoid falling into local optimum;while ensuring the convergence accuracy,the iteration speed of the algorithm is greatly improved.The improved sparrow search algorithm is compared with the original sparrow search algorithm and spider monkey optimization algorithm through simulation,and it is found that the improved sparrow search algorithm has the highest recognition accuracy and faster convergence,more suitable for solving PMSM parameter identification problems.Secondly,in order to improve the dynamic decoupling effect of PMSM,a current error compensation observer combined with deviation decoupling strategy is designed,and the corresponding filter is designed according to the actual need for decoupling transfer function of the system by using the resonance controller characteristics.The current error compensation observer is divided into two parts: one is to observe the error disturbance inside the system,which is mainly achieved by observing the difference between the output value of the current and the theoretical value,and the other is to compensate the observed current error disturbance by feedback to achieve the offset of the disturbance inside the system.The advantages of the proposed method over other decoupling strategies are compared through theoretical derivation as well as simulation,and the effectiveness of the proposed method is demonstrated.Finally,to further verify the effectiveness of the proposed strategy,a DSP-based PMSM control platform is designed,and the hardware and software of the control platform are designed.The advantages of the proposed PMSM multi-parameter identification method in terms of identification accuracy and convergence speed are experimentally verified and analyzed,as well as the feasibility of the proposed decoupling control strategy to decouple the internal coupling problem of the PMSM system.