Research On Deadbeat Current Predictive Control Of Permanent Magnet Synchronous Motor

Permanent magnet synchronous motor(PMSM)is increasingly used in the modulation process of dynamic systems due to its advantages such as low loss and wide speed range.For example,it has been widely used in servo systems such as industrial production,aerospace processing,and numerical control robots.In most situations,it is often desirable for the control system to have a fast torque response,which allows the corresponding speed control system to have the advantage of high dynamic performance.Current loop and speed loop are two very important parts in the control system of permanent magnet synchronous motor.From the torque equation,it can be seen that torque is closely related to current.The current loop of the system is located at the innermost side,which can determine the magnitude of the electromagnetic torque output by the motor,thereby affecting the control performance of the system.It is a key factor reflecting the servo drive performance.Therefore,the focus of this thesis is on the current loop control of the servo system.In recent years,the research on high-performance current loop control strategies has become a hot spot of motor control,and the corresponding advanced algorithms have been continuously improved and developed in the technological revolution.This thesis takes the surface mounted permanent magnet synchronous motor as the control object,and establishes its related coordinate transformation and mathematical model.Aiming at the disadvantages of vector control current loop,such as poor dynamic performance,cumbersome PI parameter setting,and low accuracy,this thesis studies current predictive control of electric motors.Through analysis,it can be seen that deadbeat current predictive control algorithm is widely used due to its advantages such as fast response and no need for complex parameter calculations,but it is highly dependent on system parameters.When motor parameters are perturbed,the control performance of traditional deadbeat current predictive control systems will be weakened,resulting in poor robustness of the system.Based on simulation analysis,it is further determined that the disturbances of the system inductance and flux linkage parameters are the key factors affecting the control performance of the algorithm.Aiming at the problem of poor system stability,the idea of robust current control is added to the traditional deadbeat current control,and a weighting coefficient is introduced to reduce the impact of a single error,thereby improving the robustness of the system and achieving the design of predictive models with better anti-interference performance.However,when robust control operates stably,due to the introduction of robust factors,there will be greater static current errors in steady-state conditions.In order to eliminate various current static errors caused by motor parameter offsets and the introduction of robustness factors,a Lomborg disturbance observer is designed and added to achieve feedforward compensation.After the numerical compensation of the disturbance by the observer,the steady-state static error of the dq axis current also decreases,and the robustness of the control system is improved;Subsequently,an improved algorithm for eliminating static current difference is proposed to improve the problem caused by motor parameter offset.The simulation model was built using the Simulink environment and relevant simulation experiments were completed on a semi physical simulation experimental platform,verifying the feasibility of two improved control strategy theories.The results indicate that the designed improved current control algorithm can achieve good control of the current loop and achieve stable current tracking when the parameters are offset.Therefore,the system has good static and dynamic control performance,and its robustness is improvedThis thesis has 44 pictures,7 tables and 73 references.