Linear induction motors are superior to rotary induction motors in direct drive systems because they can generate direct forward thrust force independent of mechanical transmission.However,due to the large air gap and...Linear induction motors are superior to rotary induction motors in direct drive systems because they can generate direct forward thrust force independent of mechanical transmission.However,due to the large air gap and cut-open magnetic circuit,their efficiency and power factor are quite low,which limit their application in high power drive systems.To attempt this challenge,this work presents a system-level optimization method for a single-sided linear induction motor drive system.Not only the motor but also the control system is included in the analysis.A system-level optimization method is employed to gain optimal steady-state and dynamic performances.To validate the effectiveness of the proposed optimization method,experimental results on a linear induction motor drive are presented and discussed.展开更多
Model predictive controls(MPCs) with the merits of non-linear multi-variable control can achieve better performance than other commonly used control methods for permanent magnet synchronous motor(PMSM) drives.However,...Model predictive controls(MPCs) with the merits of non-linear multi-variable control can achieve better performance than other commonly used control methods for permanent magnet synchronous motor(PMSM) drives.However,the conventional MPCs have various issues,including unsatisfactory steady-state performance,variable switching frequency,and difficult selection of appropriate weighting factors.This paper proposes two different improved MPC methods to deal with these issues.One method is the two-vector dimensionless model predictive torque control(MPTC).Two cost functions(torque and flux) and fuzzy decision-making are used to eliminate the weighting factor and select the first optimum vector.The torque cost function selects a second vector whose duty cycle is determined based on the torque error.The other method is the two-vector dimensionless model predictive current control(MPCC).The first vector is selected the same as in the conventional MPC method.Two separate current cost functions and fuzzy decision-making are used to select the second vector whose duty cycle is determined based on the current error.Both proposed methods utilize the space vector PWM modulator to regulate the switching frequency.Numerical simulation results show that the proposed methods have better steady-state and transient performances than the conventional MPCs and other existing improved MPCs.展开更多
文摘Linear induction motors are superior to rotary induction motors in direct drive systems because they can generate direct forward thrust force independent of mechanical transmission.However,due to the large air gap and cut-open magnetic circuit,their efficiency and power factor are quite low,which limit their application in high power drive systems.To attempt this challenge,this work presents a system-level optimization method for a single-sided linear induction motor drive system.Not only the motor but also the control system is included in the analysis.A system-level optimization method is employed to gain optimal steady-state and dynamic performances.To validate the effectiveness of the proposed optimization method,experimental results on a linear induction motor drive are presented and discussed.
文摘Model predictive controls(MPCs) with the merits of non-linear multi-variable control can achieve better performance than other commonly used control methods for permanent magnet synchronous motor(PMSM) drives.However,the conventional MPCs have various issues,including unsatisfactory steady-state performance,variable switching frequency,and difficult selection of appropriate weighting factors.This paper proposes two different improved MPC methods to deal with these issues.One method is the two-vector dimensionless model predictive torque control(MPTC).Two cost functions(torque and flux) and fuzzy decision-making are used to eliminate the weighting factor and select the first optimum vector.The torque cost function selects a second vector whose duty cycle is determined based on the torque error.The other method is the two-vector dimensionless model predictive current control(MPCC).The first vector is selected the same as in the conventional MPC method.Two separate current cost functions and fuzzy decision-making are used to select the second vector whose duty cycle is determined based on the current error.Both proposed methods utilize the space vector PWM modulator to regulate the switching frequency.Numerical simulation results show that the proposed methods have better steady-state and transient performances than the conventional MPCs and other existing improved MPCs.
