单边短初级长次级直线感应电机己普遍应用于低速磁悬浮的驱动系统。由于在动态纵向边端效应影响下等效电路不对称，单边直线感应电机（single-sided linear inductionmotor，SLIM）的一些参数非线性变化。传统的应用于旋转电机的无速度...单边短初级长次级直线感应电机己普遍应用于低速磁悬浮的驱动系统。由于在动态纵向边端效应影响下等效电路不对称，单边直线感应电机（single-sided linear inductionmotor，SLIM）的一些参数非线性变化。传统的应用于旋转电机的无速度传感器方法不再适用。首先分析了SLIM的M／T轴等效电路，选择次级磁链作为速度观测器状态变量。根据李雅普诺夫系统稳定性判据，推导出适用于SLIM的无速度传感器辨识；然后，采用反馈广义积分观测器控制稳态辨识速度的双幅脉振幅值；引入虚拟期望变量（virtualdesiredvariable，VDV）法，利用估算速度参与SLIM的恒滑差频率矢量控制。仿真与实验对所提控制算法的有效性和实用性进行了验证，所得结论可为磁悬浮的无速度传感器控制提供参考。展开更多
A 5-degrees-of-freedom bearingless induction motor is a multi-variable,nonlinear and strong-coupled system．In order to achieve rotor suspension and operation steadily,it is necessary to realize dynamic decoupling con...A 5-degrees-of-freedom bearingless induction motor is a multi-variable,nonlinear and strong-coupled system．In order to achieve rotor suspension and operation steadily,it is necessary to realize dynamic decoupling control among torque and suspension forces．In the paper,a method based on α-th order inverse system theory is used to study dynamic decoupling control．Firstly,the working principles of a 3-degrees-of-freedom magnetic bearing and a 2-degrees-of-freedom bearinglees induction motor are analyzed, the radial-axial force equations of 3-degrees-of-freedom magnetic bearing,the electromagnetic torque equation and radial force equations of the 2-degrees-of-freedom bearingless induction motor are given,and then the state equations of the 5-degrees-of-freedom bearingless induction motor are set up．Secondly,the feasibility of decoupling control based on dynamic inverse theory is discussed in detail,and the state feedback linearization method is used to decouple and linearize the system．Finally,linear control system techniques are applied to these linearization subsystems to synthesize and simulate．The simulation results have shown that this kind of control strategy can realize dynamic decoupling control among torque and suspension forces of the 5-degrees-of-freedom bearingless induction motor,and that the control system has good dynamic and static performance．展开更多
An adaptive current compensation control for a single-sided linear induction motor(SLIM) with nonlinear disturbance observer was developed. First, to maintain t-axis secondary component flux constant with consideratio...An adaptive current compensation control for a single-sided linear induction motor(SLIM) with nonlinear disturbance observer was developed. First, to maintain t-axis secondary component flux constant with consideration of the specially dynamic eddy-effect(DEE) of the SLIM, a instantaneously tracing compensation of m-axis current component was analyzed. Second,adaptive current compensation based on Taylor-discretization algorithm was proposed. Third, an effective kind of nonlinear disturbance observer(NDOB) was employed to estimate and compensate the undesired load vibrations, then the robustness of the control system could be guaranteed. Experimental verification of the feasibility of the proposed method for an SLIM control system was performed, and it showed that the proposed adaptive compensation scheme with NDOB could significantly promote speed dynamical response and minimize speed ripple under the conditions of external load coupled vibrations and unavoidable feedback control variables measured errors, i.e., current and speed.展开更多
A feature extraction and fusion algorithm was constructed by combining principal component analysis(PCA) and linear discriminant analysis(LDA) to detect a fault state of the induction motor.After yielding a feature ve...A feature extraction and fusion algorithm was constructed by combining principal component analysis(PCA) and linear discriminant analysis(LDA) to detect a fault state of the induction motor.After yielding a feature vector with PCA and LDA from current signal that was measured by an experiment,the reference data were used to produce matching values.In a diagnostic step,two matching values that were obtained by PCA and LDA,respectively,were combined by probability model,and a faulted signal was finally diagnosed.As the proposed diagnosis algorithm brings only merits of PCA and LDA into relief,it shows excellent performance under the noisy environment.The simulation was executed under various noisy conditions in order to demonstrate the suitability of the proposed algorithm and showed more excellent performance than the case just using conventional PCA or LDA.展开更多
文摘单边短初级长次级直线感应电机己普遍应用于低速磁悬浮的驱动系统。由于在动态纵向边端效应影响下等效电路不对称，单边直线感应电机（single-sided linear inductionmotor，SLIM）的一些参数非线性变化。传统的应用于旋转电机的无速度传感器方法不再适用。首先分析了SLIM的M／T轴等效电路，选择次级磁链作为速度观测器状态变量。根据李雅普诺夫系统稳定性判据，推导出适用于SLIM的无速度传感器辨识；然后，采用反馈广义积分观测器控制稳态辨识速度的双幅脉振幅值；引入虚拟期望变量（virtualdesiredvariable，VDV）法，利用估算速度参与SLIM的恒滑差频率矢量控制。仿真与实验对所提控制算法的有效性和实用性进行了验证，所得结论可为磁悬浮的无速度传感器控制提供参考。
基金Supported by National Natural Science Foundation of P.R.China(50575099,60674095)
文摘A 5-degrees-of-freedom bearingless induction motor is a multi-variable,nonlinear and strong-coupled system．In order to achieve rotor suspension and operation steadily,it is necessary to realize dynamic decoupling control among torque and suspension forces．In the paper,a method based on α-th order inverse system theory is used to study dynamic decoupling control．Firstly,the working principles of a 3-degrees-of-freedom magnetic bearing and a 2-degrees-of-freedom bearinglees induction motor are analyzed, the radial-axial force equations of 3-degrees-of-freedom magnetic bearing,the electromagnetic torque equation and radial force equations of the 2-degrees-of-freedom bearingless induction motor are given,and then the state equations of the 5-degrees-of-freedom bearingless induction motor are set up．Secondly,the feasibility of decoupling control based on dynamic inverse theory is discussed in detail,and the state feedback linearization method is used to decouple and linearize the system．Finally,linear control system techniques are applied to these linearization subsystems to synthesize and simulate．The simulation results have shown that this kind of control strategy can realize dynamic decoupling control among torque and suspension forces of the 5-degrees-of-freedom bearingless induction motor,and that the control system has good dynamic and static performance．
基金Project(114601034)supported by the Scholarship Award for Excellent Doctoral Students Granted by the Ministry of Education of ChinaProject(61273158)supported by the National Natural Science Foundation of China
文摘An adaptive current compensation control for a single-sided linear induction motor(SLIM) with nonlinear disturbance observer was developed. First, to maintain t-axis secondary component flux constant with consideration of the specially dynamic eddy-effect(DEE) of the SLIM, a instantaneously tracing compensation of m-axis current component was analyzed. Second,adaptive current compensation based on Taylor-discretization algorithm was proposed. Third, an effective kind of nonlinear disturbance observer(NDOB) was employed to estimate and compensate the undesired load vibrations, then the robustness of the control system could be guaranteed. Experimental verification of the feasibility of the proposed method for an SLIM control system was performed, and it showed that the proposed adaptive compensation scheme with NDOB could significantly promote speed dynamical response and minimize speed ripple under the conditions of external load coupled vibrations and unavoidable feedback control variables measured errors, i.e., current and speed.
基金Project supported by the Second Stage of Brain Korea 21 ProjectProject(2010-0020163) supported by Priority Research Centers Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education,Science and Technology
文摘A feature extraction and fusion algorithm was constructed by combining principal component analysis(PCA) and linear discriminant analysis(LDA) to detect a fault state of the induction motor.After yielding a feature vector with PCA and LDA from current signal that was measured by an experiment,the reference data were used to produce matching values.In a diagnostic step,two matching values that were obtained by PCA and LDA,respectively,were combined by probability model,and a faulted signal was finally diagnosed.As the proposed diagnosis algorithm brings only merits of PCA and LDA into relief,it shows excellent performance under the noisy environment.The simulation was executed under various noisy conditions in order to demonstrate the suitability of the proposed algorithm and showed more excellent performance than the case just using conventional PCA or LDA.
