一种基于无功功率的感应电动机复合转速辨识方法

针对基于无功功率的模型参考自适应转速辨识法在动态过程中存在的收敛慢问题,提出了一种基于无功功率的复合转速辨识方法,即将基于无功功率的直接计算法和MRAS转速辨识法相结合的方法。实验结果证明,该方法既具有直接计算法的快速跟随能力,又有MRAS转速辨识法的强抗干扰能力,且算法简单,所用电动机参数少,容易在工程中实现。     

无轴承异步电机转子磁场定向MRAS转速辨识

为了准确辨识无轴承异步电机的转速,提出一种改进转子磁链估算电压模型。以改进转子磁链估计模型为基础,构建了无轴承异步电机的转子磁场定向模型参考自适应(MRAS)无速度传感器矢量控制系统,并通过Matlab/Simulink进行了系统仿真分析。仿真结果表明:基于所提出的转子磁链改进模型,可有效避免纯积分环节初值和累计误差等影响,获得较高的转速辨识精度。所提出的转子磁链辨识模型和MRAS无速度传感器矢量控制系统是有效和可行的。     

一种基于改进反电动势的模型参考自适应转速辨识方法

构建了感应电动机的数学模型;在分析基于无功功率的模型参考自适应的稳定性的基础上,提出一种基于改进反电动势的模型参考自适应转速辨识的新方法,即重新构造模型参考自适应转速辨识的误差信号,该信号包括反电动势的误差矢量与转子磁链矢量的叉乘和点乘,通过误差信号辨识出电动机的转子转速。试验结果表明,该方法克服了基于反电动势的模型参考自适应在停车制动及发电状态下的不稳定性问题。     

基于MARS的无速度传感器矢量控制系统研究

应用模型参考自适应方法,对无速度传感器矢量控制系统的转速估计进行研究,针对常规速度辨识器中的基准模型易受积分初值和漂移问题的影响,造成辨识结果不准确的问题,埘传统的MRAS方法进行了改进研究,并且以此为基础,采用基于神经网络的模型参考自适应辨识电机转速。MATLAB仿真结果表明,应用这种估计算法计算量小,收敛速度快,估计转速能很好地跟踪实际转速,而且具有很好的鲁棒性。     

An MRAS method for sensorless control of induction motor over a wide speed range

This paper addresses the problem of wide speed range sensorless control of induction motor.The proposed method is based on model reference adaptive system (MRAS),in which the current model serves as the adjustable model,and a novel hybrid model integrating the modified voltage model (MVM) and high-frequency signal injection method (HFSIM) are established to serve as the reference model.The HFSIM works together with MVM to improve the performance of the rotor speed and rotor flux position estimation at low speed,whereas at high speed,the MVM acts alone.In addition,a rotor resistance online estimation scheme is proposed to update the rotor resistance contained in the adjustable model and to ensure the estimation accuracy further.Experimental results show that the proposed MRAS method is very effective from low to high speed range,including zero speed.     

Two new control signal approaches for obtaining the MRAS-CDM and a real-time application

The coefficient diagram method (CDM) is one of the most effective control design methods. It creates control systems that are very stable and robust with responses without the overshoot and small settling time. Furthermore, all control parameters of the control systems are changed by varying some adjustment parameters in CDM depending on the demands. The model reference adaptive systems (MRAS) are the systems that follow and change the control parameters according to a given model reference system. There are several methods to combine the CDM with MRAS. One of these is to use the MRAS parameters as a gain of the CDM parameters. Another is to directly use the CDM parameters as the MRAS parameters. In the industrial applications, the system parameters can be changed frequently, but if the controller, by self-tuning, recalculates and develops its own parameters continuously, the system becomes more robust. Also, if the poles of the controlled systems approach the jw axis, the response of the closed-loop MRAS becomes more and more insufficient. In order to obtain better results, CDM is combined with a self-tuning model reference adaptive system. Systems controlled by a model reference adaptive controller give responses with small or without overshoot, have small settling times, and are more robust. Thus, in this paper, a hybrid combination of MRAS and CDM is developed and two different control structures of the control signal are investigated. The two methods are compared with MRAS and applied to real-time process control systems.     

Comparison of two novel MRAS based strategies for identifying parameters in permanent magnet synchronous motors

Two model reference adaptive system (MRAS) estimators are developed for identifying the parameters of permanent magnet synchronous motors (PMSM) based on the Lyapunov stability theorem and the Popov stability criterion, respectively. The proposed estimators only need online measurement of currents, voltages, and rotor speed to effectively estimate stator resistance, inductance, and rotor flux-linkage simultaneously. The performance of the estimators is compared and verified through simulations and experiments, which show that the two estimators are simple, have good robustness against parameter variation, and are accurate in parameter tracking. However, the estimator based on the Popov stability criterion, which can overcome parameter variation in a practical system, is superior in terms of response speed and convergence speed since there are both proportional and integral units in the estimator, in contrast to only one integral unit in the estimator based on the Lyapunov stability theorem. In addition, the estimator based on the Popov stability criterion does not need the expertise that is required in designing a Lyapunov function.     

MW级永磁同步电机无速度传感器矢量控制研究

针对电机实验站对永磁同步电机实验电源系统新的控制要求,研究了MW级永磁同步电机无速度传感器矢量控制方法,提出了一种基于模型参考自适应的速度辨识方法。该方法仅用q轴磁链误差信号构建自适应率辨识转速信息。首先通过Matlab仿真证明了该方法的有效性,然后进行了半直驱隐极式永磁同步风力发电机对拖实验,结果证明该方法能较好地辨识电机转速。     

基于MRAS矢量控制系统的速度估算

应用模型参考自适应方法，对无速度传感器矢量控制系统的转换估计进行研究，提出了一种以电机的瞬时无功作为辅助变量，鲁棒性良好的新型MRAS速度估计方案。仿真实验表明，应用这种估计算法计算量小，收敛速度快，速度估计精确。     

基于MRAS理论的无速度传感器直接转矩控制调速系统

在MRAS理论基础上讨论了三种转速观测法,并将其中既能实现电机转速辨识又能实现定子电阻辨识的交互式模型参考自适应方法应用于异步电机无速度传感器直接转矩控制中.该方法能较好地解决电机参数变化对调速精度和系统稳定性影响的问题,仿真结果证明了该方案的有效性.