Intelligent Adaptive Fractional Order Sliding Mode Control For Permanent Magnet Synchronous Motor

With the rapid development of industry,permanent magnet synchronous motor（PMSM）is widely used in industrial robots,precision instruments,CNC machine tools,electric vehicles and other fields with its advantages of high efficiency,high power density and fast response speed.However,PMSM control system is a strong coupling,multivariable nonlinear system,which is easy to be affected by parameters variation and external disturbances,so it is difficult to accurately control the speed in a wide speed range.Therefore,a scheme combining intelligent optimization algorithm and adaptive fractional sliding mode control was proposed to improve the robustness and tracking performance of PMSM control system.Firstly,the basic structure and working principle of PMSM were introduced,the causes of uncertainties in the system were analyzed,and the dynamic mathematical model of PMSM with uncertainties was established.According to the principle of vector control,PMSM vector control system was established.Then,an adaptive fractional order sliding mode control method based on nonlinear disturbance observer was designed to solve the problem that PMSM control system is vulnerable to uncertainties such as parameters variation and external disturbances.The combination of adaptive control and fractional order sliding mode control could effectively suppress the chattering phenomenon,adjust the switching gain in real time,and improve the tracking accuracy of the system.In order to further weaken the influence of external disturbances on the system,a nonlinear disturbance observer was used to estimate the external disturbances in real time,and the observed value was introduced into the adaptive fractional sliding mode controller.Simulation results show that the proposed method effectively improves the tracking performance and anti-interference ability of the system.Finally,aiming at the problem of complex controller and many parameters,particle swarm optimization（PSO）and differential evolution algorithm（DE）were combined to adjust the control parameters.PSO algorithm and DE algorithm are very suitable for solving nonlinear problems and parameter optimization problems involving complex equations.An adaptive PSO algorithm based on ring neighborhood topology was designed,and the inertia weight and acceleration factors were adjusted according to the evolution factor to improve the convergence accuracy.In order to overcome the defect of fixed control parameters of DE algorithm,the scaling factor and crossover probability were updated adaptively according to the iterative results.Then the two intelligent optimization algorithms were combined to further improve the optimization ability.The fitness function was designed according to the PMSM speed tracking error and overshoot.The simulation results show that the hybrid algorithm of PSO and DE can get a better combination of control parameters,which is helpful to improve the comprehensive performance of the control system.