A New Sensorless Vector Control Method of PMSM

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基于高频注入法的pmsm无位置传感器控制

基于高频注入法的pmsm无位置传感器控制

摘要永磁同步电机(PMSM)因其体积小、效率高、能量密度高等特点,已经在工业生产、日常生活、新能源汽车等领域中得到了广泛的应用。

常用的永磁同步电机控制策略都需要实时获知转子的位置,目前一般是通过角度传感器来获得转子位置,但与此同时,带有角度传感器的控制系统往往需要控制系统提供额外的接口电路,而且需要考虑传感器的稳定性和成本等问题,一些工作情况比较恶劣的情况下甚至不允许系统加装传感器。

鉴于这些原因,无位置传感器的PMSM控制成为当前需要解决的一个问题。

本文针对这一问题,研究了基于高频信号注入法的PMSM无位置传感器的控制策略。

本文首先分析了PMSM的基本结构以及数学模型,然后介绍了空间矢量脉冲宽度调制(SVPWM)的理论。

在SVPWM的基础上,介绍了PMSM的矢量控制,即通过坐标变换解耦,把控制系统的励磁分量和转矩分量单独控制。

在矢量控制系统的大框架下,介绍了高频信号注入法的基本工作原理,即在电机的基波电压中注入幅值远低于直流总线电压、频率远高于转子电角度频率的正弦信号,然后对高频信号激励下的定子电流进行采样,通过滤波器获得含有转子位置的高频信号,再通过一系列数学运算解算出转子位置。

在这些理论基础上,建立了旋转高频注入法和脉振高频注入法的MATLAB/Simulink模型,仿真结果表明两种高频注入法都能较好的跟踪转子位置。

设计了以MKV46F256VLH16为核心的PMSM无位置传感器控制系统,并在图形化上位机FreeMASTER平台运行了基于脉振高频注入法的实验,得到了详细的实验波形和数据。

论文最后通过仿真和实验结果,得出结论。

关键词:永磁同步电机 无位置传感器 矢量控制 高频注入法AbstractPermanent Magnet Synchronous Motor(PMSM) has been widely used in the field of industrial production, daily life, new energy vehicles and so on due to its small volume, high efficiency, high energy density, etc. In general, common control strategy for PMSM needs real-time rotor position, which is usually obtained by rotor position sensor. Meanwhile, control system with position sensor should offer additional interface electric circuit, and the stability and cost of position sensor should be taken into consideration. In addition, position sensor could not be installed in harsh situation. In consideration of these reasons, sensorless control system for PMSM need to be proposed. This paper aims at this issue and studies strategy of sensorless control on PMSM based on high frequency signal injection.This paper analyzes the basic structure and mathematic model of PMSM, and introduces the theory of Space Vector Pulse Width Modulation(SVPWM). B ased on SVPWM, vector control system of PMSM is introduced, which decouples excitation and torque variable using coordinates transform, so two variables could be controlled alone. Basic principle of high frequency signal injection is introduced based on the frame of vector control. Sinusoidal signal is injected into motor basic voltage, whose amplitude is far below dc bus voltage and frequency is far higher than rotor electrical frequency. After sampling stator current which is generated by high frequency injection, high frequency signal with rotor position information could be obtained by filter. Rotor position could be solved with mathematic operation by high frequency signal. Based on these theoretical analysis, MATLAB/Simulink model of rotating high frequency signal injection and fluctuating high signal frequency injection are built, which have superior performance on rotor position trace. At last, a sensorless PMSM control system experiment platform is designed, which uses the MKV46F256VLH16 chip as the core component, and experiment of high frequency signal injection is operated on graphic upper-computer FreeMASTER, and detailed experimental waveforms and data are obtained.Finally, this paper draw a conclusion based on simulation and experiment.Keywords:PMSM; Sensorless; Vector Control; High Frequency Signal Injection目录摘要 (I)Abstract ................................................................................................................................................... I I 目录. (III)第一章绪论 (1)1.1研究背景 (1)1.2国内外发展现状及分析 (3)1.3本文主要研究内容 (5)第二章PMSM的数学模型与控制 (7)2.1永磁同步电机的基本结构 (7)2.2 PMSM的数学模型 (8)2.3 SVPWM算法的原理与实现 (12)2.4 PMSM的矢量控制 (15)2.5本章小结 (17)第三章高频信号注入法的PMSM无位置传感器控制 (18)3.1 高频激励下的PMSM数学模型 (18)3.2 旋转高频电压注入法的PMSM无传感器控制 (20)3.3 脉振高频电压注入法的PMSM无传感器控制 (23)3.3.1 脉振高频电压注入法的基本原理 (23)3.3.2 基于跟踪观测器的转子位置估计方法 (25)3.3.3 基于PLL转子位置估计方法 (26)3.4 转子极性判断 (28)3.5 本章小结 (30)第四章高频注入法的Simulink仿真 (32)4.1 基于SVPWM的FOC控制算法仿真 (32)4.1.1 SVPWM算法仿真模块 (32)4.1.2 基于SVPWM的FOC控制算法仿真 (35)4.2旋转高频电压注入法系统仿真 (37)4.3脉振高频电压注入法系统仿真 (41)4.4 两种高频注入法的比较 (43)4.5 本章小结 (43)第五章PMSM无传感器矢量控制系统设计 (45)5.1 系统硬件结构 (45)5.1.1 主控制芯片 (46)5.1.2 电源电路 (46)5.1.3 IPM功率电路 (48)5.1.4 信号采集电路 (49)5.1.5 通信电路 (51)5.2 系统软件结构 (51)5.2.1 主程序设计 (52)5.2.2 中断子程序设计 (52)5.2.3 SVPWM程序设计 (53)5.2.4 PID程序设计 (54)5.2.5 脉振高频注入法检测转子位置程序设计 (55)5.3 基于高频注入法的无位置传感器永磁同步电机矢量控制系统试验 (56)5.4本章小结 (60)结论与展望 (61)参考文献 (63)攻读硕士学位期间取得的研究成果 (67)致谢 (68)第一章绪论第一章绪论1.1研究背景能源一向是人类生活、工业生产必不可缺的物质根本。

Sensorless Maximum Power Point Tracking Control in Wind Energy Generation using PMSG

Sensorless Maximum Power Point Tracking Control in Wind Energy Generation using PMSG
Indian Institute of Technology Kanpur, U.P. - 208016, India Email: suresh snv@yahoo.co.in
Partha Sarathi Sensarma, Member, IEEE Department of Electrical Engineering Indian Institute of Technology Kanpur, U.P. - 208016, India Email: sensarma@iitk.ac.in

power flow control. In this paper the control of the active rectifier is presented. The load is assumed to be perfect sink for energy generated by the WECS. The application of ‘T’ type filter topology for wind energy applications is also proposed which is shown in Fig.2. Here, Vabc,t, Vabc,i represent three phase voltages at terminals of the generator and inverter respectively. The voltage control mode of operation is possible with this topology as the capacitor voltage manifests as a state in the dynamical equations (5-7) of the system. With this configuration the current switching ripple can be effectively attenuated, and hence the electromagnetic torque oscillations. Further, it attenuates the voltage switching harmonics at the terminals of generator and hence the voltage and dielectric stresses due to switching harmonics. The disadvantages with these stresses are discussed in [10].

变频器的控制模式 VF 矢量控制

变频器的控制模式 VF 矢量控制

PUBLICMotor Control ModesVariable Frequency DrivesBasic Control TypesVolts/Hertz Control(V/Hz)Sensorless Vector Control (SVC )Flux Vector Control (FVC)Field Oriented Control (FVC/FOC)PWM AC Drive Block DiagramPower Conversion Unit (PWM)This figure shows a block diagram of the power conversion unitin a PWM drive. In this type of drive, a diode bridge rectifierprovides the intermediate DC circuit voltage. In the intermediateDC circuit, the DC voltage is filtered in a LC low-pass filter.Output frequency and voltage is controlled electronically bycontrolling the width of the pulses of voltage to the motor..Motor Current▪Flux Producing Component -Id ▪Torque Producing Component -Iq ▪Total Current (FLA)-ItSo what is the relationship?22IqId It +=IdLoad 1Load 2IqItItVolts/Hertz Control▪Volts/Hertz control is a basic control method▪Requires the least setup▪Varies the voltage and frequency at the outputof the drive▪Mainly used for fan and pump applications▪Most commonly used motor control modeDrive maintains a linear relationship between Voltage & Frequency460 Vac / 60 Hz =7.667 V/Hz230 Vac / 60 Hz =3.833 V/HzCustom V/Hz or Pump/Fan curveV/HZ Controlpoor torque at low speedsPer Unit TorqueSensorless Vector▪Sensorless Vector does …▪uses the V/Hz core▪provides better breakaway torque than V/Hz▪provides better torque throughout the speed range than V/Hz▪requires an autotune procedure to be performed on the motor▪Sensorless Vector does not…▪require a feedback device▪regulate torque▪regulate speedSensorless VectorSensorless Vector Speed vs. Torque▪Flux Vector control provides more precise speed and torque control with dynamic response using a voltage angle and a voltage magnitude.▪Flux current and torque are independently controlled and speed is indirectly controlled by a torque reference signal.▪Used when high performance speed regulation or torque regulation is required.Sensorless Flux VectorFlux Vector Drive OperationField Oriented Control•Field Oriented Control drives provide the best speed and torqueregulation available for AC motors by controlling both the flux andtorque components of the motor.•It provides DC like performance for AC motors, and is well suited for typical DC retrofit applications.•All current Rockwell Automation / Allen-Bradley products (FOC and FVC)Vector Vs Field Oriented Control▪Vector Control▪Acknowledges that motor current is the vector sum of thetorque and flux currents and uses this information toprovide better control of motor speed/torque.▪Field Oriented Control▪The ability to independently control the flux and torque in amotor for the purpose of accurate torque and powercontrol.▪Force Technology uses patented, high bandwidth currentregulators in combination with an adaptive controller, toseparate and control the motor flux and torque.Force Technology w/FeedbackForce Technology -SensorlessFOC Drive OperationAbove is a plot of a drive using the Sensorless version of Force Technology. Notice that thetorque output is consistent from no load to full load over a very wide speed range.Performance ComparisonTorque and Speed ControlTorque per Ampere ComparisonThe result is that a motor run at low loads will dissipate higher losses when controlled by aVolts/Hertz drive. At slower speeds, this could cause unnecessary motor overheating.PUBLIC Questions。

Realization of sensorless vector control system based on MRAS with DSP

Realization of sensorless vector control system based on MRAS with DSP

Proceedings of the 27th Chinese Control ConferenceJuly 16-18, 2008, Kunming,Yunnan, ChinaRealization of Sensorless Vector Control System Based onMRAS with DSPHuang Yushui, Liu DanDepartment of Electrical and Automatic Engineering,Nanchang University,Nanchang 330031, P. R. ChinaE-mail: huangyushui@Abstract: Sensorless vector control of an induction motor drive essentially means vector control without any speed sensor. It is possible to estimate the speed signal from machine terminal voltages and currents with the help of a DSP. Sensorless vector controlled drives are commercially available at this time. A sensorless vector control system based on model referencing adap-tive system (MRAS) is introduced. The calculation model of rotor flux was analyzed. The sensorless vector control system was realized based on the chip of TMS320LF2407A. The simulation and test result verified that the method of sensorless vector control is feasible and valid.Key Words: MRAS, Sensorless vector control, Speed estimation1INTRODUCTIONIn the field of AC machines and their system, the speed sensor is usually used to the closed-loop control system for the realization of precise control. Not only the speed sensor increased the system cost, but reduced the system stability and the reliability. Moreover the speed sensor’s work precision is influenced by outside factor and other working conditions. These questions has been limited the application in the field of AC machines and their system, therefore the sensorless vector control sys-tem research and the application receive more and more attention.According to the three-phase asynchronous machines model and the vector control principle, the model refer-encing adaptive system(MRAS) is used to carry on the calculation to the rotor speed, the SVPWM control is carried by the control circuit based on TMS320 LF2407A to the asynchronous machines.According to the system software simulation as well as the system hardware debugging, the result has confirmed that the method of sensorless based on MRAS is feasible and the valid.2SENSORLESS VECTOR CONTROL STRATEGYThe invention of vector control and the demonstration that an induction motor can be controlled like a sepa-rately excited dc motor, brought a renaissance in the high-performance control of ac drives. Undoubtedly, vector control and the corresponding feedback signal processing, particularly for modern sensorless vector control, are complex and the use of powerful micro-computer or DSP is mandatory.2.1Rotor flux estimationThe rotor flux estimation may obtain through the rotor flux current model and the rotor flux voltage model.Rotor flux current model: i sĮand i sȕ are obtained in stationary reference frame according to the actual survey three-phase stator current through 3/2 transfor-mation. The asynchronous machines rotor flux current model equations are obtained according to the mathe-matical model [1]in stationary reference frame. The equations are:)(11ψψβααωirrsmrirTiLT p−+=(1))(11ψψαββωirrsmrirTiLT p++=(2) Where:ȌȖĮi isĮ-axis rotor flux linkage in rotor flux current model;ȌȖȕiis ȕ-axis rotor flux linkage in rotor flux current model;L m is mutual inductance;Ȧ is the rotor speed;R r is rotor resistance;T r =L r/R r is the rotor current time constant.The rotor flux current model in stationary reference frame is built based on the MATLAB software as shown in Fig. 1.Fig. 1 Rotor flux current modelWhere:is_abc is the surveyed three-phase stator current;psir_Į is Į-axis rotor flux linkage;psir_ȕ is ȕ-axis rotor flux linkage.Rotor flux voltage model: the stator flux and the rotor flux are calculated according to the actual voltage and current signal. Then the asynchronous machines rotor flux voltage model equations are obtained according to the mathematical model [1] in stationary reference frame. The equations are:])([iLiRuLLsssssmrvrdtαααασψ−−=³(3)691692])([i L i R u L L s s s s s mr v r dt ββββσψ−−=³ (4)where:ȌȖĮȞis Į-axis rotor flux linkage in rotor flux voltage model;ȌȖȕȞis ȕ-axis rotor flux linkage in rotor flux voltage model;u s Įis Į-axis static voltage; u s ȕis ȕ-axis static voltage; L r is rotor inductance; L s is stator inductance R s is stator resistance; ı=1-L m ²/(L s ×L r ) .The rotor flux voltage model in stationary reference frame is built based on the MATLAB software as shownin Fig. 2.Fig. 2 Rotor flux voltage modelWhere: u A , u B , u C is the surveyed three-phase stator volt-age.2.2Model referencing adaptive system (MRAS) [2-6] The speed can be calculated by the model referencing adaptive system (MRAS), where the output of a refer-ence model is compared with the output of an adjustable or adaptive model until the errors between the two mod-els vanish to zero. Consider the voltage model’s sta-tor-side equation by (3) and (4), which are defined as a reference model. The model calculates the flux vector signals, as indicate. The current model flux equations, (1) and (2), are defined as an adaptive model. This model can calculate fluxes from the input stator currents only if the speed signal Ȧis known.In designing the adaptation algorithm for MRAS, it is important to take account of the overall stability of the system and to ensure that the estimated speed will con-verge to the desired value with satisfactory dynamic characteristics. Using Popov’s criteria for hyperstability [7]for a globally asymptotically stable system, we can derive the following relation for speed estimation:))((ψψψψωαββαv r i r v r i r i p r s kk −+= (5)3SIMULATION ANALYSISThe system simulation model is built based on the soft-ware of MATLAB according to preceding text analysis.The IGBT inverter module, vector control module, speed estimation module are included in the systemsimulation model as shown in Fig.3.Fig. 3 System simulation modelThe system parameter establishment is: Three-phase asynchronous machines U N =380V , f =50Hz, P =2,R s =0.085ȍ, L ls =0.53mH, R r =0.225ȍ,L lr =0.53mH, L m =3.5mH, J =1.562kg.m ²; In the simulation system, the surveyed rotor speed value is replaced by calculated rotor speed Ȧ', the control effect based on the MRAS sensorless vector control system can be observed, simu-lation result as shown in Fig.4.a. Surveyed rotor speedb. Calculated rotor speed Fig. 4 Simulation r esultAccording to Fig.4, it can be see that the difference be-tween surveyed rotor speed and calculated rotor speed are very small, surveyed rotor speed relative smoother, as a whole calculated rotor speed can track surveyed rotor speed. Moreover, through the comparisons of fig.4, may discover: The machine from starts to stably, in the entire process calculated rotor speed and surveyed rotor speed both continuously maintained accurately and fast synchronized following; This had proven that it is feasi-ble for the three-phase asynchronous machines sensor-less vector control based on MRAS.4CONTROL CIRCUIT4.1System structureSystem structure as shown in Fig.5:693Fig. 5 System str uctur eThe overall system is a AC-DC-AC circuit, mainlycomposed by the main circuit, the protection circuit, the control circuit. The main circuit is composed by the un-controlled rectification bridge, the capacity filter circuit, the IPM inversion circuit and the drive circuit. The IPM inversion circuit carries on the inversion after rectifica-tion DC voltage, obtains three-phase AC which has ad-justable voltage and frequency to supplies machine. The DSP chip TMS320LF2407A is taken as a core in the control circuit, and constituted formidable numeral vec-tor control system. The PWM signal is controlled by the DSP chip, which carries on the vector operation to sur-vey the stator current and voltage. This system has owes the voltage examination circuit, the overvoltage exami-nation circuit as well as failure detection circuits and so on. This system movement is safer reliably.4.2System software designsThe system software is composed by the master pro-gram and the PWM interrupt service subroutine. The software and the hardware initialization work is com-pleted by the master program (Fig.6). The master pro-gram is also responsible for the parameter, owes the voltage protection, overvoltage protection, overflowcurrent protection.Fig. 6 The master programThe system software used DSP to interrupt INT1.INT1 is the timer T 1 cycle interrupt. The PWM interrupt ser-vice subroutine (Fig.7) mainly completes some timely processing work, including electric current, voltage sig-nal sampling, coordinate transformation, speed estimate and adjustment algorithm, SVPWM wave productionand so on.Fig. 7 PWM interrupt service subroutine4.3Experiments analyzesThe system hardware debugging is carried based on TMS320LF2407A in IMCD2407 platform. The IMCD2407 platform is offered by Intelligent Motion Digital Signal Processing Co,.LTd. It may obtain the reference voltage U ref of modulation wave in the View/Graph window of IMCD2407 platform. When modulation percentage M =0.5 and 1, the reference volt-age U ref of modulation wave in sampling period Ts asshown in Fig. 8.a.M=0.5b.M =1Fig. 8 Modulation waveThe above test result conforms to the SVPWM voltagemodulation wave of the SVPWM theoretical analysis, proved this article uses the three-phase asynchronous machines sensorless vector control method in the prac-tical application based on MARS is feasible and valid.5CONCLUSIONA method based on the MRAS sensorless vector control is proposed in the paper. The method establishes the self-adaptive model according to the rotor flux current model and the rotor flux voltage model, carries on the calculation to the rotational speed and realizes the reac-tion control. The control system based on the MRAS sensorless vector control has many merits such as the structure is simple, the load computation is small, reali-zation is easy and the machine parameter is little af-fected.The TMS320LF2407A is taken as the core structure of system to realize the sensorless vector control based on MRAS, the simulation and test result verified that the method of sensorless vector control is feasible and valid. REFERENCES[1]GAO J D, WANG X H, LI F H. Analysis of AC machinesand their system (second edition) [M]. Beijing: Tsinghua University Press, 2005. [2]BIMAL K B.Modern Power Electronics and AC Drives[M].Prentice Hall PTR Prentice-Hall, 2002.[3]RAJASHEKARA K, KAWAMURA A, MATSUSE K .et al.Sensorless Control of AC Drives, IEEE Press, NY, 1996. [4]LIN F J , WAI R J, KUO R H et al. A comparative study ofsliding model and model reference adaptive speed observers for induction motor drive. Electric Power Systems Re-search[J] ,1998,(44): 163-174.[5]YANG G, CHEN B S. Review the Methods for the SpeedSensor-less Control of Induction Motor [J]. Electrical drive 2001(3):3-8.[6]LIAO Y, ZHANG F R. Research of Sensorless Vector Con-trol System and Speed Estimation [J]. Transactions of China electrotechnical society 2004(2):36-40.[7]LANDAU Y D, Adaptive Control-The Model ReferencingApproach, Marcel Dekker, 1979.694。

感应电机二阶滑模次优算法定子磁链观测器设计

感应电机二阶滑模次优算法定子磁链观测器设计

感应电机二阶滑模次优算法定子磁链观测器设计潘月斗;陈泽平;郭映维【摘要】提出了基于二阶滑模次优算法的感应电机定子磁链观测方法,设计了定子磁链观测器,并应用到感应电机直接转矩控制中.本文设计的磁链观测器,通过准确的跟踪电流及其变化率,从而实现对转子磁链的准确估算,然后利用转子磁链与定子磁链的关系,估算出定子磁链.由于本文设计的定子磁链观测器是一个多输入多输出(MIMO)系统,稳定性分析非常复杂,为此将磁链估算误差的微分看作扰动处理,从而将MIMO的观测器模型分解成两个独立的单输入单输出(SISO)系统,简化了稳定性分析.将该观测器用于感应电机直接转矩控制中,达到了很好的控制效果.仿真和实验验证了该方法的有效性.【期刊名称】《控制理论与应用》【年(卷),期】2015(032)005【总页数】5页(P641-645)【关键词】感应电机;二阶滑模;次优算法;电流观测;磁链观测;直接转矩控制【作者】潘月斗;陈泽平;郭映维【作者单位】北京科技大学自动化学院,北京100083;北京科技大学钢铁流程先进控制教育部重点实验室,北京100083【正文语种】中文【中图分类】TM343感应电机被广泛应用于工农业生产、国防、科技及社会生活等各个方面,随着直接转矩控制和矢量控制技术的出现,使其逐渐进入了伺服控制领域[1].相对于矢量控制,直接转矩控制方法直接把转矩作为被控量,并由电流和定子磁链估算,无需进行磁场定向和矢量变换,更为简单和实用,具有快速的动态响应能力[2].直接转矩控制中,定子磁链观测值的精确度直接影响控制效果[3].定子磁链观测的基本方法有电压模型法和电流模型法.电压模型法结构简单,观测时仅需确定定子电阻.但是电压模型法在运算过程中需开环积分(纯积分),微小的直流偏移误差和初始值误差都将导致积分饱和[4].电流模型法可解决电压模型积分漂移和无法建立初始磁链的问题,但观测精度与转速相关,易受电动机转速变化的影响[5].为了更好的观测磁链,已提出了很多方法,如滑模变结构方法[6–7]、自适应方法[8]、卡尔曼滤波器方法[9–10]、神经网络方法[11]等.相比其他方法,滑模变结构方法对系统的不确定性因素具有较强的鲁棒性和抗干扰性,同时控制设计简单,物理上易于实现,因此得到广泛应用.但是在实际应用中,滑模变结构控制也存在一些问题,其中最主要的是抖振现象[12].近些年提出的高阶滑模控制理论[13],是对传统滑模控制理论的进一步推广.相比传统滑模,高阶滑模不仅保持了传统滑模的优点,同时抑制了系统的抖振,除去了相对阶的限制,并且提高了控制精度.二阶滑模控制是目前应用最广泛的高阶滑模控制方法,因为它的控制器结构简单且所需要的信息不多.二阶滑模控制中常见的4种算法有:twisting(螺旋)算法、sub-optimal(次优)算法、prescribed convergence law(给定收敛律)算法和Super-Twisting(超螺旋)算法.本文设计了一种基于二阶滑模次优算法的感应电机定子磁链观测器.将磁链估算误差的微分看作扰动处理,从而将MIMO的观测器模型分解成两个独立的SISO系统,简化了稳定性分析.将该观测器用于感应电机直接转矩控制中,达到了很好的控制效果.仿真及实验结果验证了该方法的有效性.设感应电机的磁路是线性的,忽略铁损的影响,在静止坐标系(α–β)下,感应电机的数学模型的状态方程为[14]δ=ηRs+Lmλθ;isα,isβ,usα,usβ,ψrα,ψrβ分别为α轴和β轴的定子电流、定子电压和转子磁链;ωr为转子电角速度;Ls,Lr,Lm分别为定子电感、转子电感和定转子间互感;Rs,Rr分别为定子电阻和转子电阻.定子磁链和转子磁链存在如下关系[15]:设计如下感应电机转子磁链观测器:其中:分别为定子电流和转子磁链的状态估计变量,vα和vβ为控制信号,分别为α轴和β轴的定子电流观测误差.定子电压和定子电流usα,usβ,isα,isβ都是可以检测到的,定子电压是原实际系统(感应电机)的输入量,定子电流可作为原实际系统的输出量;针对此观测器而言,定子电流检测量isα,isβ作为给定输入量(也作为干扰输入的一部分),定子电压检测量usα,usβ以及转子电角速度看作干扰输入的一部分;,作为观测器的反馈量.式(1)减式(2),可以得到定子电流和转子磁链观测误差方程电流观测误差方程写成如下形式:由式(5)可知,电流误差方程系统相对于控制信号v是1阶系统,因此可以采用二阶滑模控制,设计控制信号v,使得滑模变量s趋于零,并保持二阶滑动模态,即s==0.如果选取s=,采用二阶滑模控制,即可使得=0.二阶滑模次优算法(sub-optimal)形式如下:其中:s∗是最近的时间内,=0时s的值;k1,k2为控制参数,令s(t,x)=0为所定义的滑模面,控制目标是使系统的状态在有限时间内收敛到滑模流形s== 0.选取滑模面s=设计如下控制律:其中:对于式(5),将看作扰动处理,可将其分成α轴和β轴方向两个独立的SISO(单入单出)系统,如下:文献[16]给出了次优算法有限时间收敛的充分条件:其中Km,KM,C满足如下条件:对于本文设计的观测器系统,α轴方向分析如下:上式对时间求导,可得系统有限时间收敛的充分条件[16]如下:如果参数kα1,kα2满足式(9),则系统必能在有限时间内到达滑模面满足如下条件: β轴方向的稳定性分析同上.利用转子磁链观测器估算得到的转子磁链和定子电流,可估算定子磁链基于二阶滑模次优算法的感应电机定子磁链观测器系统框图如图1所示.为了检验所设计的基于二阶滑模次优算法的感应电机定子磁链观测器的有效性,进行了MATLAB仿真与实验.电机参数为:额定电压UN=220V,定子电阻Rs=94Ω,转子电阻Rr=83.9Ω,定子自感Ls= 5.387H,转子自感Lr=5.387H,互感Lm=5.082H,转动惯量J=0.105kg·m2.观测器控制参数为:kα1=kβ1=10,kα2=kβ2=5.电机施加220V,15Hz的三相交流电,在开环下空载运转,4s时,施加3N·m负载转矩.仿真时间7s,仿真结果如图2–5所示.从图3和图4可以看出,观测电流误差及其微分(由于实际对磁链观测误差有影响的是,所以图4实际是δ的值),在一定时间内渐近趋于0,从而说明了给二阶滑模次优算法控制的有效性.从图5可以看出,观测磁链在一定时间内达到稳定.为了验证基于二阶滑模次优算法的感应电机定子磁链观测器的有效性,将其应用到感应电机直接转矩控制中.电机参数与开环时一样,定子磁链给定值ψ=1Wb,给定转速600r/min.转速调节器采用PID控制,其中比例系数KP=10,积分系数KI= 0.001,微分系数KD=0.5.仿真时间20s,仿真结果如图6所示.为了验证二阶滑模次优算法定子磁链观测器的实际可行性,利用“电力电子与电气传动综合实验台”进行实验.实验台组成包括:功率挂箱、主控挂箱、加载控制箱、电动机、上位机,如图7所示.实验电机为鼠笼式三相异步电动机,参数与仿真时所用电机参数相同.转速给定值600r/min,实验结果如图8所示.从仿真和实验结果可以看出,二阶滑模次算法定子磁链观测器能够很好的观测定子磁链,电机转速也最终稳定在了给定值600r/min,从而证明了本文所提出的基于二阶滑模次算法的感应电机定子磁链观测器的实际可行性.本文提出的二阶滑模次优算法定子磁链观测器,首次将二阶滑模次优算法应用到感应电机定子磁链观测器设计中,并将此观测器应用到直接转矩控制中.从仿真和实验结果可以看出,该观测器能够准确的估算定子磁链,将其用于感应电机直接转矩控制中,也达到了很好的控制效果.仿真实验验证了该方法的有效性.潘月斗(1966–),男,博士,副教授,目前研究方向为交流电动机智能控制理论研究及高速高精交流电动机驱动系统的计算机数字控制系统设计,E-mail:****************;陈泽平(1989–),男,硕士研究生,目前研究方向为电气传动及自动化,E-mail:**********************;郭映维(1990–),男,硕士研究生,目前研究方向为异步电机控制理论及数字化设计,E-mail:*****************.【相关文献】[1]PELLEGRINO G,GUGLIELMI P,ARMANDO E,et al.Selfcommissioning algorithm for inverter nonlinearity compensation in sensorless induction motor drives[J].IEEE Transactions on Industry Applications,2010,46(4):1416–1424.[2]张细政,王耀南,袁小芳,等.基于滑模与自适应观测器的感应电机非线性控制新策略[J].控制理论与应用,2010,27(6):753–760.(ZHANG Xizheng,WANG Yaonan,YUAN Xiaofang,et al.New nonlinear controller forinduction motor based on sliding-mode control and adaptive observer[J].Control Theory&Applications,2010, 27(6):753–760.)[3]张猛,肖曦,李永东.基于扩展卡尔曼滤波器的永磁同步电机转速和磁链观测器[J].中国电机工程学报,2007,27(36):36–40.(ZHANG Meng,XIAO Xi,LI Yongdong.Speed and flux linkage observer for permanent magnet synchronous motor based on EKF[J]. Proceedings of the CSEE,2007,27(36):36–40.)[4]李红,罗裕,韩邦成,等.带通滤波器法电压积分型定子磁链观测器[J].电机与控制学报,2013,17(9):8–16.(LI Hong,LUO Yu,HAN Bangcheng,et al.Voltage integral model for stator flux estimator based on band-pass filter[J].Electric Machines and Control,2013,17(9):8–16.)[5]SPICHARTZ M,STEIMEL A,Stator-flux-oriented control with high torque dynamics in the whole speed range for electric vehicles[C] //Emobility-Electrical Power Train.New York:IEEE,2010:1–6.[6]LI J C,XU L Y,ZHANG Z.An adaptive sliding-mode observer for induction motor sensorless speed control[J].IEEE Transactions on Industry Applications,2005,41(4):1039–1046.[7]REHMAN H.Elimination of the stator resistance sensitivity and voltagesensorrequirementproblemsforDFOcontrolofaninductionmachine[J].IEEE Transactions on Industrial Electronic,2005,52(1): 263–269.[8]刘艳红,霍海娟,楚冰,等.感应电机转矩跟踪无源控制及自适应观测器设计[J].控制理论与应用,2013,30(8):1021–1026.(LIU Yanhong,HUO Haijuan,CHU Bing,et al.Passivity-based torque tracking control and adaptive observer design of induction motors[J].ControlTheory&Applications,2013,30(8):1021–1026.)[9]BARUT M,BOGOSYAN S,GOKASAN M.Speed-sensorless estimation for induction motors using extended Kalman filters[J].IEEE Transactions on IndustrialElectronics,2007,54(1):272–280.[10]HAQUE M E,ZHONG L,RAHMAN M F.A sensorless initial rotor position estimation scheme for a direct torque controlled interior permanent magnet synchronous motor drive[J].IEEE Transactions on Power Electronics,2003,18(6):1376–1383.[11]SIMOES M G,BOSE B K.Neural network based estimation of feedback signals for a vector controlled induction motor drive[J].IEEE Transactions on IndustryApplication,1995,31(3):620–629.[12]YOUNGK D,UTKIN V I,OZGUNER U.A control engineer’s guide to sliding mode control[J].IEEE Transactions on Control Systems Technology,1999,7(3):328–342.[13]FRIDMAN L,LEVANT A.Higher order sliding modes as a natural phenomenon in control theory[J].Robust Control via Variable Structure and LyapunovTechniques.Heidelberg,Berlin:Springer,1996: 107–133.[14]LI J,XU L,ZHANG Z.An adaptive sliding-mode observer for induction motor sensorless speed control[J].IEEE Transactions on Industry Applications,2005,41(4):1039–1046. [15]MITRONIKASED,SAFACASAN.Animprovedsensorlessvectorcontrol method for an induction motor drive[J].IEEE Transactions on Industrial Electronics,2005,52(6):1660–1668.[16]LEVANT A.Principles of 2-sliding mode design[J].Automatica, 2007,43(4):576–586.。

一种无传感器PMSM效率优化控制方法

一种无传感器PMSM效率优化控制方法

一种无传感器PMSM效率优化控制方法吕一松;李旭春;贺骥;吴正礼【摘要】提出了一种新的永磁同步电机无位置传感器正弦波控制方法。

该方法不同于传统的转子位置估算思路,而是利用以电流为基准的永磁同步电机矢量图,得到改进的效率最优策略,进而提出“强制同步-效率优化”控制方法。

文章介绍了新方法的实现方案,并借助永磁同步电机功角特性曲线证明了方法的稳定性。

方法思路清晰,复杂度低;参数适应性强;硬件系统简单,易于实现。

实验结果证明了该方法的可行性。

【期刊名称】《电工技术学报》【年(卷),期】2010(000)006【总页数】6页(P12-17)【关键词】180°直流无刷电机;正弦波永磁同步电机;无传感器;强制同步;d轴电流【作者】吕一松;李旭春;贺骥;吴正礼【作者单位】清华大学自动化系,北京100084【正文语种】中文【中图分类】TP3411 前言永磁同步电机(Permanent Magnet Synchronous Motor,PMSM)在转子轴上往往安装有位置传感器(如霍尔位置传感器、编码器、测速发电机等),传感器的使用不仅增加了成本,降低了系统的可靠性,而且受到诸如温度、湿度和振动等条件的限制,使之不能广泛适用于各种场合。

为了克服传感器给系统带来的缺陷,学者们进行了无传感器永磁同步电机控制研究,比较典型的控制方法有:直接计算法、模型参考自适应法、观测器法、高频注入法以及基于人工智能的方法等[1]。

然而,为达到电机的最优运行效率,上述方法无一例外都需对电机进行三相-两相变换(3-2变换)、估算转子位置,进而跟随转子位置输出适当的同步电压或电流(本文称其为“跟随同步”控制方法)。

但是,这种跟随同步方法的3-2变换和估算转子位置的过程,要在一个或几个 PWM周期(时间一般小于200µs)内完成,有时还需使用迭代算法,程序编写复杂,计算量大,硬件要求高。

同时,转子位置估计算法中用到的电机参数如电阻、磁通对温度和电流等工作环境非常敏感,算法鲁棒性差。

Sensorless+Control+of+PMSM+Based+on+Adaptive+Sliding+Mode+Observer

Sensorless+Control+of+PMSM+Based+on+Adaptive+Sliding+Mode+Observer

ˆα + f (iα ) < 0 ΔA ⋅ iα i ˆβ + f (iβ ) < 0 ΔA ⋅ iβ i
2 MODELS AND OBSERVER
In stationary (α , β ) reference frame, the mode for PMSM is characterized by (1)
diα R 1 uα = − iα + eα + dt L L L diβ R uβ 1 = − iβ + eβ + dt L L L eα = −λ0ω e sin(θ e )
speed can be derived as
& ≈ λ B( S sin θ ˆ − S cosθ ˆ) ˆ ω e 0 1 2
And (10) can be rewritten as
(10)
− R L . Because the variation of L
& ≈ λ B ⋅ [(i ˆ − (i ˆ] ˆα − iα ) sinθ ˆβ − iβ ) cosθ ˆ ω e 0
s e e
ˆα di R 1 ˆα + 1 e ˆα + uα + f (iα ) = (− + ΔA)i dt L L L ˆβ di R 1 ˆβ + 1 e ˆβ + uβ + f (iβ ) = (− + ΔA)i dt L L L
(2) Where superscript “ ^ ” represents the estimated quantities, “—”represents the error quantities, ΔA is the variation of

Sensorless vector control of induction motors at very low speed using a nonlinear inverter model and

Sensorless vector control of induction motors at very low speed using a nonlinear inverter model and

Sensorless Vector Control of Induction Motors at Very Low Speed Using a Nonlinear Inverter Model and Parameter IdentificationJoachim Holtz,Fellow,IEEE,and Juntao QuanAbstract—The performance of vector-controlled induction motor drives without speed sensor is generally poor at very low speed.The reasons are offset and drift components in the acquired feedback signals,voltage distortions caused by the non-linear behavior of the switching converter,and the increased sensitivity against model parameter mismatch.New modeling and identification techniques are proposed to overcome these problems.A pure integrator is employed for stator flux estima-tion which permits high-estimation pensation of the drift components is done by offset identification.The nonlinear voltage distortions are corrected by a self-adjusting inverter model.A further improvement is a novel method for on-line adaptation of the stator resistance.Experiments demonstrate smooth steady-state operation and high dynamic performance at extremely low speed.Index Terms—Induction motor,low-speed operation,parameter identification,sensorless control,vector control.I.I NTRODUCTIONC ONTROLLED induction motor drives without speedsensor have developed as a mature technology in the past few years.However,their performance at very low speed is poor.The main reasons are the limited accuracy of stator voltage acquisition,the presence of offset and drift compo-nents in the acquired voltage signals,their limited bandwidth, offsets and unbalances in the current signals,and the increased sensitivity against model parameter mismatch.These deficiencies degrade the accuracy of flux estimation at low speed.The dynamic performance of a sensorless drive then deteriorates.Sustained operation at very low speed becomes im-possible as ripple components appear in the machine torque and the speed starts oscillating,eventually leading to instable oper-ation of the system.Paper IPCSD02–025,presented at the2001Industry Applications Society Annual Meeting,Chicago,IL,September30–October5,and approved for publication in the IEEE T RANSACTIONS ON I NDUSTRY A PPLICATIONS by the Industrial Drives Committee of the IEEE Industry Applications Society. Manuscript submitted for review October15,2001and released for publication May10,2002.J.Holtz is with the Electrical Machines and Drives Group,University of Wup-pertal,42097Wuppertal,Germany(e-mail:j.holtz@).J.Quan is with the Danaher Motion Group,Kollmorgen-Seidel,Duesseldorf, Germany(e-mail:jquan@).Publisher Item Identifier10.1109/TIA.2002.800779.II.S OURCES OF I NACCURACY AND I NSTABILITYA.Estimation of the Flux Linkage VectorMost sensorless control schemes rely directly or indirectly on the estimation of the stator flux linkagevectoris the stator resistance.Time is normalizedasis the nominal stator frequency[3].The addedsymbol in (1)represents all disturbances such as offsets,unbalances,and other errors that are contained in the estimated inducedvoltage(2)where is the coupling factor of the rotorwindings,is the total leakage flux vector.The estimation of one of the flux vectors according to(1)or (2)requires performing an integration in real time.The use of a pure integrator has not been reported in the literature.The reason is that an integrator has an infinite gain at zero frequency.The unavoidable offsets contained in the integrator input then make its output gradually drift away beyond limits.Therefore,instead of an integrator,a low-pass filter usually serves as a substitute.A low-pass filter has a finite dc gain which eases the drift problem, although drift is not fully avoided.However,a low-pass filter in-troduces severe phase angle and amplitude errors at frequencies around its corner frequency,and even higher errors at lower fre-quencies.Its corner frequency is normally set to0.5–2Hz,de-pending on the existing amount of offset.The drive performance degrades below stator frequencies2–3times this value;the drive becomes instable at speed values that correspond to the corner frequency.Different ways of compensating the amplitude and phase-angle errors at low frequencies have been proposed[4]–[7].0093-9994/02$17.00©2002IEEEOhtani [4]reconstructs the phase-angle and amplitude error pro-duced by the low-pass filter.A load-dependent flux vector refer-ence is synthesized for this purpose.This signal is transformed to stator coordinates and then passed through a second low-pass filter having the same time constant.The resulting error vector is added to the erroneous flux estimate.Although the benefits of this method are not explicitly documented in [4],improved performance should be expected in an operating range around the corner frequency of the low-pass filter.With a view to improving the low-speed performance of flux estimation,Shin et al.[5]adjust the corner frequency of the low-pass filter in proportion to the stator frequency,while com-pensating the phase and gain errors by their respective steady-state values.It was not demonstrated,though,that dynamic op-eration at very low frequency is improved.Hu and Wu [6]try to force the stator flux vector onto a circular trajectory by propor-tional plus integral (PI)control.While this can provide a correct result in the steady state,it is erroneous at transient operation and also exhibits a large error at startup.A practical application of this method has not been reported;our investigations show loss of field orientation following transients.B.Acquisition of the Stator VoltagesThe induced voltage,which is the signal to be integrated for flux vector estimation,is obtained as the difference between the stator voltage and the resistive voltage drop across the ma-chine windings.When a voltage-source inverter (VSI)is used to feed the machine,the stator voltages are formed by pulse trains having a typical rise time of 2–10kV/,whereis the fundamental componentofcaused by the switching characteristics of the inverter.C.Acquisition of the Stator CurrentsThe stator currents are usually measured by two Hall sensors.They are acquired as analog signals,which are subsequently digitized using A/D converters.The sources of errors in this process are dc offsets and gain unbalances in the analog signal channels [9].After the transformation of the current signals to synchronous coordinates,dc offsets generate ac ripple compo-nents of fundamental frequency,while gain unbalances produce elliptic current trajectories instead of circular trajectories.The disturbance in the latter case is a signal of twice the fundamentalfrequency.Fig.1.Effect of a dc offset in one of the current signals on the performance of a vector-controlled drivesystem.Fig.2.Effect of a gain unbalance between the acquired current signals on the performance of a vector-controlled drive.The following oscillograms demonstrate the effect of such disturbances on the performance of a vector-controlled drive system.The respective disturbances are intentionally intro-duced,for better visibility at a higher signal level than would normally be expected in a practical implementation.Fig.1shows the effect of 5%dc offset in one of the current signals on the no-load waveform ofthe.The drive is operated is at astator frequency of 2Hz.The transformed current signals gen-erate oscillations in the torque-producing current .Resulting from this are torque pulsations of 0.06nominal value,and cor-responding oscillations in the speedsignal,where isthe power factor of the motor.Fig.2shows the same signals under the influence of 5%gain unbalance between the two current channels.Oscillations of twice the stator frequency are generated in the torque-producing current,and also in the speed signal.D.Estimation of the Stator ResistanceAnother severe issue,in addition to the integration problem and to the nonlinear behavior of the inverter,is the mismatch be-tween the machine parameters and the respective model param-eters.In particular,adjusting the stator resistanceHOLTZ AND QUAN:SENSORLESS VECTOR CONTROL OF INDUCTION MOTORS1089Fig.3.Forward characteristics of the power devices.flux estimation,and for stable operation at very low speed.The actual valueofand an averagedifferentialresistance [12].The variations with temperatureof the thresholdvoltageof about equal magnitude to all the threephases,and it is the directions of the respective phase currents that determine their signs.The device thresholdvoltage(4)where.Thesectorindicator is a unity vector that indicates the re-spectivein thecomplex plane.The locations are determined by the respective signs of the three phase currents in (3),or,in other words,by a maximumof.The referencesignalof the stator voltagevectoris less than its referencevalue,and of the resis-tive voltage drops of the power devicesthrough1090IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS,VOL.38,NO.4,JULY/AUGUST2002(a)(b)(c)Fig.4.Effect at PWM of the forward voltagesuof the power semiconductors.(a)Switching state S.(c)Switching state S);the dotted linesindicate the transitions at which the signs of the respective phase currents change.Notethatis the resulting threshold voltage vector.We have,therefore,from (4),the unusualrelationshipis one parameter of the invertermodel.It is determined during a self-commissioning process from the distortions of the reference voltagevectorand of the reference voltage vector are acquiredwhile using the current controllers to inject sinusoidal currents of very low frequency into the stator windings.In such condi-tion,the machine impedance is dominated by the stator resis-tance.The stator voltages are then proportional to the stator cur-rents.Any deviation from a sinewave of the reference voltages that control the pulsewidth modulator are,therefore,caused by the inverter.As an example,an oscillogram of the distorted referencevoltagewaveformsand ,measured at sinusoidal currents ofmagnitude ,is shown in Fig.7.The amplitude of the fundamental voltage is very low which is owed to the low frequency of operation.The distortions of the voltage waveforms in Fig.7are,therefore,fairly high.They are predominantly caused by the dead-time effect of the ing such distorted voltages to represent the stator voltage signal in a stator flux estimator would lead to stability problems at low speed.Accurate inverter dead-time compensation [13]is,therefore,mandatory for high-performance applications.Fig.8shows the same components of the reference voltagevectoraccording to (4);the locationsofare shown in Fig.4.It follows from (4)that both the larger step change and the amplitudeofhave the magnitude4/3from thewaveformof(or )in Fig.8appears quite inaccurate.A better method is subtracting the fundamentalcomponent from,e.g.,,which then yields a square-wave-like,stepped waveform as shown in Fig.9.The fundamental component iseasily extracted from a set of synchronous samplesofby fast Fourier transform.The differential resistance of the powerdevices,in (6),es-tablishes a linear relation between the load current and its in-fluence on the inverter voltage.Functionally,it adds to the re-sistance)is estimated by an online tuning process described inSection III-D.HOLTZ AND QUAN:SENSORLESS VECTOR CONTROL OF INDUCTION MOTORS1091(a)(b)Fig.6.Effect of inverter nonlinearity.The trajectory u represents the average stator voltage (switching harmonics excluded).(a)At motoring.(b)Atregeneration.Fig.7.Effect of inverter dead time on the components of the voltage vectoruas in Fig.7;inverteroperated with dead-time compensation.C.Stator Flux EstimationThe inverter model (6)is used to compensate the nonlinear distortions introduced by the power devices of the inverter.The model estimates the stator voltagevector(8)is the estimated effective offset voltage vector,while is theestimated stator field angle.The offset voltagevectorin (7)is determined such that the estimated stator fluxvector rotates close to a circular trajectory in the steady state,which follows from (7)and from the right-hand side of (8).To enable the identificationofin (8),the stator field angle is estimatedas(9)as illustrated in the right portion of Fig.10.The magnitude of the stator flux linkage vector is then obtainedas1092IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS,VOL.38,NO.4,JULY/AUGUST2002Fig.10.Signal flow graph of the inverter model and the stator flux estimator.The gain constantserve this purpose in a satisfactorymanner.The stator frequency signal is computed byis determined,for instance,with reference to[2]of the stator current,as shown in Fig.11.We haveand,consequently,.Of the superscripts,component of the vector product of the statorvoltage and current vectors.The system equation,for example given in[3],isHOLTZ AND QUAN:SENSORLESS VECTOR CONTROL OF INDUCTION MOTORS1093Fig.12.Signal flow graph of the stator resistance estimator.wherecomponent of all terms in(19)and assumingfieldorientation,,wehave10toa n d=!=wp w wp p f p p w1094IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS,VOL.38,NO.4,JULY/AUGUST2002Fig.16.Identification of the stator resistance,demonstrated by a30%stepincrease of the resistance value.Fig.17.Reversal of speed between the set-point values w=60:04;torqueis constant at50%nominal value.the speed is negative.Finally,the performance of the stator re-sistance identification scheme is demonstrated in Fig.17.Thestator resistance is increased by30%in a step-change fashion.The disturbance causes a sudden deviation from the correct fieldangle,which produces a wrong value.The new value ofHOLTZ AND QUAN:SENSORLESS VECTOR CONTROL OF INDUCTION MOTORS1095 Juntao Quan was born in Jiangxi,China,in1964.Hereceived the B.Eng.degree from Jiangxi PolytechnicCollege,Nanchang University,Nanchang,China,the M.Eng.degree from Northeast-Heavy MechanicInstitute,Yanshan University,Qinhuangdao,China,and the Ph.D.degree from Wuppertal University,Wuppertal,Germany,in1983,1989,and2002,respectively,all in electrical engineering.He was an Assistant Electrical Engineer for threeyears at the Nanchang Bus Factory,Nanchang,China.From1989to1994,he was a Lecturer at YanshanUniversity.During this time,he also worked on various projects for applicationsof power electronics.In1995,he joined the Electrical Machines and Drives Lab-oratory,Wuppertal University,where he worked and studied toward the Ph.D.degree.In June2000,he joined the Danaher Motion Group,Kollmorgen-Seidel,Duesseldorf,Germany.His main interests are in the areas of adjustable-speeddrives,microprocessor-embedded real-time control,power electronics applica-tions,and advanced motion control.。

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In order to verify the performance of the proposed controller in practice, an experimental test bed is integrated. The experimental setup consists of three main components: A 200 watts permanent magnet synchronous motor, the required power inverter, and a DSP board. The setup is integrated by TechnoSoft Co., in which a software interface is build into the system. The software enables the user to initialize different hardware connections, as well as to emulate, debug and test the program. The compiled program is downloaded into the DSP via the serial port of the computer. The DSP used in the emulator board is from 2407 series of Texas Instrument manufactured DSP’s. This board accommodates connection to the inverter through a cable. The measured current and command signals are conveyed through it. The inverter produces the required three-phase voltage needed for the synchronous motor by
1
Introduction
Permanent magnet motors are widely used in industrial applications, because of their superior advantages. High performance, low inertia, high torque to current ratio, high power factor, and almost no need for maintenance are among the important advantages of these type of motors causing their extensive use in different applications. However, the need of position or velocity sensor in order to apply effective vector-control algorithms, is one of their main constraints. Therefore, vector-control methods in the absence of any position or speed sensor, have been investigated by many researchers [1,2,3]. In most of the methods the main proposed alternative is the estimation of the motor position or velocity. In some methods (indirect methods) [4], first the estimation of velocity is performed and then the trigonometric values, which are required for the vector control, are calculated. In some other methods [5], the required trigonometric values are directly estimated from motor state equations. Estimation theory and especially Extended Kalman Filter method is extensively used in indirect methods [6]. However, Flux equations are the base of trigonometric value determination in direct methods [7]. In both methods the state equations are derived in the rotor coordinate system. Hence, because of the use of synchronous coordinate in the estimation procedure, usually the estimation error propagation is
A New Sensorless Vector Control Method for Permanent Magnet Synchronous Motor without Velocity Estimator
Hamid D. Taghirad*, N. Abedi and E. Noohi
Department of Electrical Engineering K.N. Toosi University. of Technology P.O. Box 16315-1355, Tehran, Iran * taghirad@
observed in practice. This problem is magnified in the presence of noise, or inaccurate knowledge of the motor parameters [6,7]. In this paper a novel method is proposed, in which the modeling and control of the motor is derived in a new coordinate system. Due to the characteristics of the derived model, there is no need to estimate the position or velocity. The speed of rotation of this frame is ω * (reference Speed) instead of ω r ; therefore, all the required trigonometric equation can be derived and implemented in a completely determined frame. The significance of this change of coordinate is elaborated in next sections. One of the main important advantages of this method is its capability to control the motor at very low velocities. The method can be categorized as a Lyapunov-based control method, in which the closed loop system is designed such that its asymptotic stability, in the sense of Lyapunov, is guaranteed. In other words the controller is designed to regulate the system about its equilibrium state. Hence, the variation of the Equilibrium State of the system is constrained to remain close to the desired trajectory. Satisfying this condition guarantees the tracking performance of the system [8]. Similar Lyapunov-based control methods for vector control of a synchronous motor, are examined by few researchers [9,10].
its DC voltage command input within 0 to 36 volts. The speed of the board is 50 kHz, which limits the frequency of producible PWM signal. There exists a current sensor, which converts the current from ±6.33 Amps to the range of [0-3.3] Volts. Finally the Synchronous motor used in the setup is from 3441 series produced by Pittman Co. whose technical specifications are given in Table 1. Fig 1 illustrates the experimental setup. Table 1, Permanent Magnet Motor Specs
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