Permanent Magnetic Synchronous Motor Speed Tracking Control Based On Improved Fractional Order Sliding Mode Variable Structure

In the 21 st century,with the continuous progress of technology in the field of materials,the research on permanent magnet materials has been continuously developed in the fields of industrial production,aerospace,civil and military.However,due to the external and parameter time-varying disturbance,and PMSM is a multivariable,nonlinear,and strongly coupled system,which causes its parameters are difficult to identify,the anti-interference ability is poor,and the speed tracking accuracy is not high.Based on Popov hyperstability theory of MRAS and RBF neural network of fractional order sliding mode,this thesis further studies the problems of PMSM for parameter identification and speed tracking control.The main work of the thesis is as follows:(1)In view of the fact that the electrical parameters are easily affected by parameter time-varying and external disturbances,a model reference adaptive system(MRAS)parameter identification method based on Popov hyperstability theory is studied.First,established a mathematical model of PMSM,then,a suitable reference model and adjustable model are established,and the parameter adaptive law of Popov hyperstability theory is used to identify the values of main electrical parameters of PMSM such as inductance,permanent magnet flux and stator resistance.Finally,the simulation results show the availability,and the identification error is less than 2%,provides a guarantee for the speed control later.(2)In view of the fact that PMSM drive system is susceptible to parameter time-varying,external interference and other factors,a fractional order sliding mode control method based on RBF neural network is studied.First,a fractional order system was introduced and the fractional order sliding surface was established.Then,in order to improve the static performance and chattering effects of the system,used the saturation function as a switching function;At the same time,the RBF neural network is used to approximate the external interference,and the estimated interference is introduced into the fractional order sliding mode controller,which is designed to form a composite controller.Finally,the simulation results show that the proposed method can suppress interference,alleviated chattering and improve the accuracy of speed tracking.(3)In order to further alleviate the system chattering and reduce the tracking error of the system,an improved fractional order sliding mode control method is proposed.First,established the traditional generalized sliding mode and complementary sliding surface,combined into a new sliding surface.Then,the RBF neural network approximation interference is introduced,a new composite controller is formed,and the stability and convergence of the composite controller are verified by Lyapunov’s stability criterion,compared with the normal fractional order sliding mode,it was found that this method obviously inhibits the overtones,further alleviated the chattering,enhances the robustness of the system,and improves the tracking accuracy.Finally,the simulation and experiment show the validity of the new composite controller.Through the research of the above control strategies,this thesis provides a new way for engineers in the field of motor control to solve the problems of PMSM speed control.