Observer-based Random Command Filtering Fuzzy Control Of Permanent Magnet Synchronous Motor

The permanent magnet synchronous motors(PMSMs)are used as the power source of electric vehicle(EV)because of their low loss,fast starting speed,high efficiency and light weight.However,the driving system of PMSMs are highly coupled,variable and highly nonlinear.Therefore,the control of PMSMs have attracted many scholars’ attention.In order to solve the control problem of PMSMs,the relevant control methods are applied to PMSMs,such as backstepping control and Hamilton control.However,these control strategies hardly consider the problems of stochastic disturbances and iron losses.During the operation of PMSMs,stochastic disturbances will be caused by voltage stochastic surge and other factors.In addition,the existence of damping torque and magnetic saturation makes some motor parameters of PMSMs change randomly.Therefore,these stochastic disturbances will affect the control effect of PMSMs,and then reduce the control accuracy of the system.At the same time,for the PMSMs in the stochastic nonlinear systems of electric vehicle,a large amount of iron losses will inevitably occur in the normal operation of the motor due to no-load or light load,which will cause permanent demagnetization due to the temperature rise of the motor.Therefore,it is very important to consider the iron losses when controlling the PMSMs in the stochastic nonlinear systems of electric vehicle.This paper combines backstepping control,command filtering control method,state observer and fuzzy logic system to control PMSMs in the stochastic nonlinear systems of electric vehicle.The main research results are as follows.(1)The problem of tracking control for a class of stochastic nonlinear systems is studied.An adaptive fuzzy control strategy based on observer technique and command filtering control method is introduced.Firstly,the command filtering technique and fuzzy logic system are introduced to solve the problem of “explosion of complexity” and deal with the nonlinear terms in the stochastic system.Secondly,when the state of stochastic nonlinear system is not measurable,the fuzzy state observer is constructed to estimate the unmeasurable states in the system.On the basis of the above technology,an adaptive fuzzy command filtering controller based on observer technology is constructed and combined with the introduced stochastic control theories to control stochastic nonlinear systems.Finally,the stability of the system is analyzed by constructing the quartic Lyapunov function.(2)An adaptive fuzzy controller based on observer technique and command filtering method is proposed for PMSMs stochastic nonlinear systems.Firstly,a fuzzy reducedorder observer is constructed to estimate the rotor angular position and the rotor angular velocity of the PMSMs stochastic nonlinear systems.Then,the output signal of the low-pass second-order filter is used to approximate the virtual control signal by using the command filtering error compensation technology.The problem of “explosion of complexity” in the calculation process is handled and the influence of the filter error is reduced.Finally,the stability of the PMSMs stochastic nonlinear systems is analyzed by Lyapunov function,and the effectiveness of the proposed adaptive fuzzy control method based on observer technique and command filtering method is verified by using Matlab simulation.(3)The fuzzy controller of PMSMs driving system in electric vehicle stochastic nonlinear system based on fuzzy observer and command filtering technology is proposed.Firstly,the mathematical model of PMSMs in the stochastic nonlinear systems of electric vehicle is established.Secondly,the fuzzy reduced order observer is constructed to estimate the rotor angular velocity and position of PMSMs in the stochastic nonlinear system of electric vehicle.Then,the command filtering technology and error compensation technology are combined to solve the problem of "explosion of complexity" and reduce the influence of filtering error.In the end,the quartic Lyapunov function is constructed to analyze the stability of the system,and the simulation results of Matlab show that the proposed adaptive fuzzy control method based on observer and command filtering technology can overcome the influence of stochastic disturbances and iron losses,and has practical application value.(4)The proposed control method is compared with the dynamic surface control method through Matlab comparison simulation.The simulation results show that the proposed control method is more robust and the tracking effect is better.