Advanced sensorless drive technique for multiphase BLDC Motors
超触觉技术系统问世 无需碰触或携带任何设备
【 ４ 】 李壮 云． 液压元件与系统 ［ Ｍ］ ． 北京 ： 机械 工业 出版社 ，
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【 ５ 】韩桂 华． 液压 系统设计技巧 与禁忌 ［ Ｍ］ ． 北京 ： 化学工业
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【 ６ 】王 春行． 液 压 控制 系统 ［ Ｍ ］ ． 北京： 机 械 工业 出 版社 ，
觉 到显示 屏上 的 内容 ，而且能 在触 摸前 接收 到看不 见 的信 息 。
士 利用超声辐射力向用户发射触觉感受还是一门新技术。该研究提出了一种超触觉理论， 即一种能在空中产生触觉反馈的 士
工系 统设计 。卡 特解 释说 ，在空 中传播 的超 声波会 产 生不 同的压 力 ，如果 许 多超 声 波 同时 到达 同一 位 置 ，就 会 在该 点 产 生 明显 的压 力变 化 。超声转 换器 阵列 能对 空 中 目标施 加压 力 ，通 过屏 幕 投射 出触 觉 感受 ，直 接作 用 于用 户 手上 。一 系列 超 声转 换 器
运行 。
液压绞车的恒张力控制有 两种方 式。一种是采用 溢流 阀或减压 阀控制系统相对恒定的压力 ，而达到控
制液压 马达扭矩恒定 的 目的 ，这种方式有较大的功率 损失 ，适用 于小功率 的绞 车 ，优点是系统简单 、控制 容易 ，可以和其他液压设 备共用液压源 ，减少投资 。 第二种是采用恒压伺服变量 系统来控制定量或变 量液压 马达 ，由于采用 了压力补偿变量泵 ，使系统在 保持和释放阶段时液压泵处于恒压状态 ，保证 了马达 的恒扭矩 ，这种系统 的功率损失小 ，一般应用 于大功 率 的液压绞犁 。 本 文作 者对第 一种 系统的设计 原理进行 了详细地 分析 ，可以作为类似系统的设计 参考。 参 考文献 ：
变频空调压缩机电机的参数辨识
Inductance model-based sensorless control of the switched reluctance motor drive at low speed
Inductance Model-Based Sensorless Control of the Switched Reluctance Motor Drive at Low Speed Hongwei Gao,Member,IEEE,Farzad Rajaei Salmasi,Member,IEEE,and Mehrdad Ehsani,Fellow,IEEEAbstract—A new sensorless control scheme for the switched reluctance motor(SRM)drive at low speed is presented in this paper.The incremental inductance of each active phase is estimated using the terminal measurement of this phase.The esti-mated phase incremental inductance is compared to an analytical model,which represents the functional relationships between the phase incremental inductance,phase current,and rotor position, to estimate the rotor position.The presented sensorless control scheme requires neither extra hardware nor huge memory space for implementation.It can provide accurate rotor position infor-mation even as the magnetic characteristics of the SRM change due to bined with other inductance model-based sensorless control techniques,the proposed method can be used to develop an inductance model-based sensorless control scheme to run the SRM from standstill to high-speed.Simulation and experimental results are presented to verify the proposed scheme. Index Terms—Inductance model,low speed,sensorless,switched reluctance motor(SRM).I.I NTRODUCTIONT HE SWITCHED reluctance motor(SRM)drive is consid-ered a promising alternative to the conventional induction motor drive due to its several salient features,such as rugged-ness,low cost,high efficiency,and simplicity in control. Rotor position information is required for high performance SRM drives.Mechanical position sensors,such as optical en-coders and resolves,have been employed in SRM drives to pro-vide the rotor position information.These mechanical sensors add to the cost and dimension and deteriorate the reliability of the SRM drive.Rotor position estimation techniques have been proposed to solve this problem[1]–[10].The proposed position estimation techniques are carried out as follows:1)estimating the phaseflux linkage or inductance using the terminal mea-surement of the SRM and2)finding the rotor position using the functional relationships between the phaseflux linkage or inductance,phase current,and rotor position.For example,in the signal injection methods presented in[1]–[7],low ampli-tude current is injected to one of the silent phases to estimate the unsaturated inductance of this phase.The estimated phase inductance is used as an index to search the rotor position fromManuscript received September11,2003;revised February4,2004.Recom-mended by Associate Editor A.Emadi.H.Gao is with the Electrical and Computer Engineering Department, Montana State University,Bozeman,MT59717USA(e-mail:hgao@).F.R.Salmasi is with Electro Standards Laboratories,Inc.,Cranston,RI02921 USA.M.Ehsani is with the Electrical Engineering Department,Texas A&M Uni-versity,College Station,TX77843USA.Digital Object Identifier10.1109/TPEL.2004.836632a two-dimensional(2-D)look-up table,which stores the rela-tionship between the unsaturated phase inductance and the rotor position.The2-D look-up table requires neither large amounts of experiment data to build nor huge memory space to store. However,these techniques cannot run the SRM at high-speed. In addition,the signal injected to the idle phase suffers from mu-tual interference from the active phases.Moreover,the methods introduced in[1]and[2]require additional hardware for im-plementation.Furthermore,extensive experimental work is re-quired to update the2-D look-up table as the magnetic charac-teristics of the SRM change due to aging.The sensorless con-trol scheme presented in[8]uses the terminal measurement of an active phase to estimate theflux linkage of this phase. The estimatedflux linkage is compared to a three-dimensional look-up table,which stores the functional relationships between the phaseflux linkage,phase current,and rotor position,tofind the rotor position.This technique neither requires extra hard-ware for implementation nor exhibits the mutual interference problem.However,it cannot drive the SRM at low speed.In ad-dition,the three-dimensional look-up table used in this method requires large amount of experimental data to constitute,huge memory space to store,and extensive experimental work to up-date when the magnetic characteristics of the SRM change due to aging.In the sensorless control method presented in[9],the terminal measurement of an active phase is used to estimate the phase inductance of this phase.The estimated phase induc-tance is compared to an analytical model,which represents the functional relationships between the phase inductance,phase current,and rotor position,to estimate the rotor position.This technique neither requires extra circuitry for implementation nor has the mutual interference problem.The analytical model employed in this method requires minimum amount of experi-mental data to develop,minimum memory space to store,and can be easily updated as the magnetic characteristics of the SRM change owing to aging.However,as that presented in[8],this method cannot run the SRM at low speed.A new sensorless control method,which uses the terminal measurement of an active phase and the analytical model pre-sented in[9]to estimate the rotor position,is proposed in this paper to drive the SRM at low speed.The outstanding features of this new sensorless technique are as follows.a)It requires neither additional hardware,nor massive ex-perimental data,nor huge memory space,for imple-mentation.b)It does not suffer from the mutual interferenceproblem.c)Since the analytical model can be easily updated toaccurately reflect the magnetic characteristics of the0885-8993/04$20.00©2004IEEESRM,the proposed sensorless control scheme can pro-vide accurate rotor position information even as themagnetic characteristics of the SRM change due toaging.d)Combined with other inductance model-based sensor-less control schemes,such as those published in[8]and[10],it can be used to develop an inductance model-based sensorless control scheme to run the SRM fromstandstill to high-speed.II.R OTOR P OSITION E STIMATION S CHEMEA.Estimation of the Phase Incremental InductanceThe phase voltage equation of the SRM is givenas(1)where,,,,and denote the phase voltage,phase current,phase resistance,phase incremental inductance,and phase self-inductance,respectively;and stand for the rotor angularposition and rotor angular speed,respectively.At low speed,the third term at the right-hand side of(1)isnegligible.Therefore,the phase voltage(1)can be simplifiedas(2)Thus,the phase incremental inductance can be givenas(3)It is evident from(3)that the phase incremental inductancecan be estimated once the phase voltage and current data areobtained.B.Development of the Phase Incremental Inductance Model1)Phase Self-Inductance Model:An analytical model,which represents the functional relationships between the phaseself-inductance,phase current,and rotor position,is presentedin[9].In this analytical model,the variation of the phaseself-inductance versus the rotor position is represented usingthe Fourier series with only thefirst three terms considered.The model for the self-inductance of phase A is givenby(4)where is the number of rotor polesand(5)(6)(7)where(8)is the aligned position inductance as a function of phasecurrent(9)is the inductance at the midway between the unaligned andaligned position as a function ofcurrent(10)is the inductance at unaligned position and is assumed to be in-dependent of the phase currentand is the degree of approxi-mation(in the presentcase yields a good accuracy).Thecoefficientsand are determined by the curvefitting methodsuch that the inductance profile obtained using(4)would exactlyfit the profile obtained from thefinite element analysis or exper-imental work.The expressions for other phase inductances are the same as(4),except with proper phase shifts.The accuracy of the above model has been verified byfiniteelement analysis and experimental work[9].Development of the above self-inductance model only re-quires measurement of the phase self-inductance at the alignedposition,unaligned position,and midway between the alignedand unaligned position.Measuring the phase inductance at thesethree rotor positions can be easily carried out.For example,foran SRM,exciting phase A,C,and B separately with suffi-cient current will move the rotor to the aligned position of phaseA,unaligned position of phase A,and the midway between thealigned and unaligned position of phase A,respectively.Afterthe rotor has been moved to each of these three positions,thephase A inductance can be measured by injecting proper currentinto phase A.The above test can be performed under the controlof the SRM drive controller when the SRM is idle.Carrying outthe test whenever the SRM is idle allows the inductance modelto be updated as the magnetic characteristics of the SRM changeowing to aging.2)Phase Incremental Inductance Model:The phase self-in-ductance model given by(4)can be used to develop a phase in-cremental inductance model,which is used for sensorless con-trol of SRM in this paper.The phase incremental inductance isderived as follows.The phaseflux linkage of the SRM is givenas(11)where stands for the phaseflux linkage of the SRM.The phaseincremental inductance,according to its definition,is givenas(12)Substituting(4)into(12)yields an analytical model rep-resenting the functional relationships between the phaseincremental inductance,phase current,and rotorposition(13)where(14)(15)(16)where(17)(18)C.Estimation of the Rotor PositionSubstituting(3)into(13)yields(19)In(19),the term can be estimate accurately according to thedc bus voltage and the gating signals of the active switches in theSRM drive inverter.The term can be calculated using thephase current data.The noisein term calculation can befiltered using the averaging method.Therefore,all the quantitiesin(19),except,can be estimated in real time once the phasecurrent is sampled.One can numerically solve(19)toestimate.In(19),the term is dominant overthe term.Therefore,the errorin estimation and sensing does not lead to a notice-able errorin estimation.From(19),one can see that estimation of the rotor positionrequires that the rotor position have one-to-one relation withthe phase incremental inductance at a given phase current.Thephase incremental inductance-phase current-rotor position char-acteristic of asample SRM is shown in Fig.1.Fig.1shows that for the sample SRM,at low current,suchasA,a unique rotor position is defined by the given phaseincremental inductance when the phase is between itsunalignedandaligned position.However,at highor medium current,suchas Aor A,the phaseexhibits the same incremental inductance at multiple rotor posi-tions,when.Therefore,at high or medium cur-rent,estimating the rotor position using the phase incrementalinductance and the current of a single phase will lead to multiplesolutions.To solve this problem,the phase incremental induc-tances of multiple active phases are estimated and used for rotorposition estimation in this work.This approach is illustrated inFig.2.The variations of the phase incremental inductance and phasecurrent of phase A(the solid curves),B(the dashed curves),C(the dotted curves),and D(the dashed-and-dotted curves)versusthe rotor position for a SRM are depicted in Fig.2,whererepresents the unaligned position of phase A.WhenFig. 1.Phase incremental inductance-phase current-rotor positioncharacteristic of a sample8=6SRM.Fig.2.Rotor position estimation using the phase incremental inductances ofmultiplephases.,phase A is active and its incremental in-ductance has one-to-one relation with the rotor position at anyphase current.Therefore,the phase incremental inductance ofphase A is estimated from its terminal measurement and used toestimate.When,though phase A is still active,the phase incremental inductance of phase A loses uniquenessversus at medium or high current,and thus,is not used to es-timate.However,when,phase B is excitedfor torque production and its phase incremental inductance hasone-to-one relation with the rotor position,and thus,is used toestimate.The phase incremental inductance of Phase C and Dare used toestimate in the same manner.The proposed rotor position estimation method requires aproper startup method such that at standstill,the phase betweenthe unaligned position and the midway between the unalignedFig.3.Simulated phase currents of the SRM.and aligned position is excitedfirst.The rotor position estima-tion scheme presented in[10]is employed to meet this require-ment.III.S IMULATION R ESULTSThe proposed senseless control scheme is studied through nu-merical simulation on asample SRM,whose rated phasevoltage,rated phase current,and rated rotor speed are100V, 6A,and1000RPM,respectively.In the simulation,each SRM phase is turned on and off at the unaligned and aligned posi-tion,respectively.The current of each SRM phase is regulated at4A,the current under which the phase incremental induc-tance has the same value at multiple rotor positions.The load of the SRM is adjusted properly so that the SRM runs at low speed (below15%of the base speed).The rotor position of the SRM is detected using the proposed position estimation scheme.The simulation results are shown in Figs.3–5.Fig.3shows the current of phase A–D.The traces in Fig.4, from the top to the bottom,depict the torque of phase A,the total torque of all the SRM phases,and the rotor speed.Fig.5 illustrates the estimated(the plus sign)and actual(the circle) commutation position of phase A.A0.65(mechanical degree) error in the rotor position estimation is observed from the quan-titative results of the simulation.Due to this error,some negative phase torque,though negligibly small,is observed in Fig.4. The simulation results presented in Figs.4and5clearly show the validity of the proposed rotor position estimation scheme.IV.E XPERIMENTAL R ESULTSThe proposed sensorless control algorithm has been imple-mented on the sample SRM mentioned in the previous section.A permanent magnet dc generator with a resistive load is used as the load of the SRM.The rotor position estimation technique,presented in[10],is used to start the SRM fromstandstill.Fig.4.Simulated torque of phase A,total torque of all the SRM phases,and rotorspeed.Fig.5.Estimated(the plus sign)and actual(the circle)commutation position of phase A.The performance of the proposed sensorless control scheme is shown in Figs.6–8.Fig.6depicts the variation of the rotor speed during the startup process.The maximum rotor speed is about130RPM in this experiment.Fig.7depicts the phase cur-rents of the SRM,which are regulated at4A in this test.In order to test the accuracy of the proposed rotor position esti-mation algorithm,an optical encoder is used to sense the actual rotor position.This sensed actual rotor position is compared to the estimated rotor position to calculate the error of the rotor position estimation.The rotor position estimation error,which is less than1mechanical degree,is depicted in Fig.8.V.C ONCLUSIONA new sensorless control scheme for the SRM drive at low speed is presented in this paper.This technique uses the terminalFig.6.Experimentally recorded rotor speed (45RPM/Div).Fig.7.Experimentally recorded current of phase A –D,from top to the bottom (1.25A/Div).Fig.8.Experimentally recorded rotor position estimation error (5/Div).measurement of the active phases and an analytical inductance model to estimate the rotor position.It neither requires addition hardware nor huge memory space for implementation.In ad-dition,this technique does not suffer from the mutual interfer-ence problem.By updating the analytical mode when the SRM is idle,the presented rotor position estimation scheme can pro-vide accurate rotor position information even as the magnetic characteristics of the SRM change due to aging.The theory ofthis sensorless control scheme is presented and veri fied by sim-ulation and experimental results.R EFERENCES[1]M.Ehsani,I.Husain,and A.B.Kulkarni,“Elimination of discrete po-sition sensor and current sensor in switched reluctance motor drives,”in Proc.IEEE Industry Applications Soc.Annu.Meeting ,vol.1,1990,pp.518–524.[2]M.Ehsani,I.Husain,S.Mahajan,and K.R.Ramani,“New modulationencoding techniques for indirect rotor position sensing in switched reluc-tance motors,”IEEE Trans.Ind.Applicat.,vol.30,pp.85–91,Jan./Feb.1994.[3]G.R.Dunlop and J.D.Marvelly,“Evaluation of a self commutedswitched reluctance motor,”in Proc.Electric Energy Conf.,1987,pp.317–320.[4]S.R.MacMinn,W.J.Rzesos,P.M.Szczensny,and T.M.Jahns,“Ap-plication of sensor integration techniques to switched reluctance motor drives,”IEEE Trans.Ind.Applicat.,vol.28,pp.1339–1344,Nov./Dec.1992.[5]S.R.MacMinn,C.M.Steplins,and P.M.Szaresny,“Switched reluc-tance motor drive system and laundering apparatus employing same,”U.S.Patent 4959596,1989.[6]W.D.Harris and ng,“A simple motion estimator for variablereluctance motors,”IEEE Trans.Ind.Applicat.,vol.IA-26,pp.237–243,Mar./Apr.1990.[7]N.H.Mvungi,houd,and J.M.Stephenson,“A new sensorlessposition detector for SR drives,”in Proc.4th Int.Conf.Power Electronics Variable Speed Drives ,1990,pp.249–252.[8]J.P.Lyons,S.R.MacMinn,and M.A.Preston,“Flux/current methodsfor SRM rotor position estimation,”in Proc.IEEE Industry Application Soc.Annu.Meeting ,vol.1,1991,pp.482–487.[9]G.Suresh,B.Fahimi,K.M.Rahman,and M.Ehsani,“Inductance basedposition encoding for sensorless SRM drives,”in Proc.IEEE Power Electronics Specialists Conf.,vol.2,1999,pp.832–837.[10]H.Gao,F.R.Salmasi,and M.Ehsani,“Sensorless control of SRM atstandstill,”in Proc.IEEE Applied Power Electronics Conf.,vol.2,2000,pp.850–856.Hongwei Gao (S ’98–M ’02)received the B.Sc.and M.Sc.degrees from Tsinghua University,Beijing,China,in 1990and 1993,respectively,and the Ph.D.degree from Texas A&M University,College Station,in 2001,all in electrical engineering.Since 2002,he has been an Assistant Professor in the Electrical and Computer Engineering De-partment,Montana State University,Bozeman.His research interests include electric machinery,motor drives,power electronics,electric and hybrid electric vehicles,renewable energy source power systems,and power quality.Dr.Gao is a member of the IEEE Power Electronics,Industry Applications,and Industrial Electronicssocieties.Farzad R.Salmasi (S ’99–M ’03)received the B.Sc.degree from Sharif University of Technology,Tehran,Iran,in 1994,the M.Sc.degree from Amir Kabir Uni-versity of Technology,Tehran,in 1997,and the Ph.D.degree from Texas A&M University,College Station,in 2002,all in electrical engineering.Currently,he is a Research Scientist with Electro Standards Laboratories,Inc.,Cranston,RI.His re-search areas include design and advanced control of electric motor drives,power electronics systems,and hybrid electric vehicles.Dr.Salmasi is a member of the IEEE Power Electronics and Industry Appli-cations Societies.Mehrdad Ehsani(S’70–M’81–SM’83–F’96)hasbeen at Texas A&M University(TAMU),CollegeStation,since1981,where he is a Professor ofelectrical engineering and Director of the AdvancedVehicle Systems Research Program.He is the authorof over300publications in pulsed-power supplies,high-voltage engineering,power electronics,andmotor drives and automotive systems.He is thecoauthor of several books on power electronics andmotor drives and a contributor to an IEEE Guide forSelf-Commutated Converters and other monographs. He is the author of over20U.S.and European Commission patents.His current research work is in power electronics,motor drives,and hybrid electric vehicles and systems.Dr.Ehsani received the Prize Paper Award in Static Power Converters and Motor Drives at the IEEE Industry Applications Society in1985,1987, and1992,the James R.Evans Avant Garde Award from the IEEE Vehicular Technology Society in2001,and the IEEE Undergraduate Teaching Award for2003.In1992,he was named the Halliburton Professor in the College of Engineering,TAMU.In1994,he was named the Dresser Industries Professor, TAMU.In2001,he was named the Dow Chemical Faculty Fellow of the College of Engineering,TAMU.He is also the Associate Editor of the IEEE T RANSACTIONS ON I NDUSTRIAL E LECTRONICS and the IEEE T RANSACTIONS ON V EHICULAR T ECHNOLOGY.He has been a member of IEEE Power Electronics Society AdCom,past Chairman of PELS Educational Affairs Committee, past Chairman of IEEE IAS Industrial Power Converter Committee,and past chairman of the IEEE Myron Zucker Student-Faculty Grant program.He was the General Chair of IEEE Power Electronics Specialist Conference for 1990.He is an IEEE Industrial Electronics Society and Vehicular Technology Society Distinguished Speaker and IEEE Industry Applications Society past Distinguished Lecturer.He is Chairman of Vehicular Technology Society Vehicle Power and Propulsion Committee,and was elected to the Board of Governors of IEEE VTS in2003.He is a Registered Professional Engineer in the State of Texas.。
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
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Sonata’s top-notch safety technology loves and protects you like family.Auto brake holdAuto child lockElectronic Stability Control (ESC) and Traction ControlPanoramic sunroofFeatures.Front personal LED Lamps Rear window curtain LED headlamps (Projection type)/Daytime running light (LED)Horizontal LED Rear Tail lights with LED turn signal lampsRear AC vents Black & Chrome coating radiator grille LED high mount stop lamp Powered rear curtain18˝Alloy wheel Shift-by-wire 6-Speed automatic transmissionWireless smartphone charging system8” Floating infotainment system displayDual zone automatic climate control Arm rest (rear seats)Rear camera and concealed boot lid buttonDriver’s 10-way powered Seat + 4 way powered Passenger SeatInterior colors.Exterior colors.Polar whiteMetallic silverHampton grayOxford blueDiamond metallic blackLeatherCamel two-tone interior1,860 1,6181,625 2,8404,900Overall width (mm) Wheel tread* (mm)Wheel tread* (mm)Wheel base (mm) Overall length (mm)Overall height (mm) 1,445Specifications.Specifications.31Specifications.3233● The above values are results from internal testing and are subject to change after validation.● Some of the equipment illustrated or described in this catalog may not be supplied as standard equipment and may be available at extra cost.● Hyundai Motor Company reserves the right to change specifications and equipment without prior notice. ● The color plates shown may vary slightly from the actual colors due to the limitations of the printing process.●Please consult your dealer for full information and availability on colors and trims.3435Dealer stamp+92-42-111 111 466hyundaipakistan 1-B, Aziz Avenue, Canal Bank Road, Gulberg V, Lahore Phone +92-42-111 111 466, ***********************Hyundai Nishat Motor (Private) Limited。
伺服 驱动电机 方法
伺服驱动电机方法When it comes to driving a servo motor, there are a few different methods that can be used. One common method is using a servo drive, which is a specialized device that is designed to control the motion of a servo motor. Servo drives typically work by receiving commands from a controller, which then converts these commands into signals that are sent to the servo motor. This allows for precise control over the speed and position of the motor, making it ideal for applications that require high levels of accuracy and precision.谈论到驱动伺服电机，有几种不同的方法可以使用。
一种常见的方法是使用伺服驱动器，这是一种专门设计用来控制伺服电机运动的设备。
伺服驱动器通常通过从控制器接收命令，然后将这些命令转换成发送给伺服电机的信号来工作。
这样可以精确控制电机的速度和位置，使其非常适合需要高精度和精确度的应用。
Another method for driving a servo motor is using pulse width modulation (PWM). PWM works by varying the width of pulses of a fixed frequency signal to control the amount of power sent to the motor. By adjusting the duty cycle of the pulses, the speed andposition of the motor can be controlled. This method is often used in applications where simplicity and cost-effectiveness are important, as it does not require complex control algorithms or specialized hardware.驱动伺服电机的另一种方法是使用脉冲宽度调制（PWM）。
A High-Speed Sliding-Mode Observer for the Sensorless Speed Control of a PMSM
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A High-Speed Sliding-Mode Observer for the Sensorless Speed Control of a PMSM
detected by a resolver or by an absolute encoder. However, these sensors are expensive and very sensitive to environmental constraints such as vibration and temperature [2]. To overcome these problems, instead of using position sensors, a sensorless control method has been developed for control of the motor using the estimated values of the position and velocity of the rotor [3]–[12]. In a conventional sliding-mode observer (SMO), a low-pass filter and an additional position compensation of the rotor are used to reduce the chattering problem commonly found in SMOs using a signum function. Currently, a sigmoid function is used for the SMO as a switching function. The observer has fast responses and has a robustness inherent in the design parameters [13], [14]. In this observer, the chattering, which happens at the observer using the signum function, has been reduced significantly [15]. The stator resistance needs to be estimated by the Lypunov function intermediate equations so as to prove the stability of the observer. The stator resistance changes during motor operation, which deteriorates the control performance, unless it is compensated for in real time [8]. The cascade control method has been proposed for the achievement of an accurate tracking performance [16], and high-gain observers have been designed to estimate the states under a scalar disturbance [17]. A hybrid terminal SMO design method is proposed to achieve sensorless drive for a PMSM [18], and a tuning method is proposed in order to obtain high-speed and high-accuracy positioning systems [19]. The general schemes of sliding-mode control have been well surveyed recently [20]. Most of the observer designs focus on the fast response and high tracking accuracy. For the fast response, the use of a sigmoid function in a boundary layer is popular. However, the observer error cannot be guaranteed to converge to zero within the boundary layer [18], [21], [22], [24]. This paper proposes a new sensorless control algorithm for a PMSM based on the new SMO which uses a sigmoid function as a switching function with variable boundary layers. Using this SMO, the position and velocity of the rotor can be calculated from the estimated back EMF [25]. Also, to overcome the sensitivity of the parameter variations in the sensorless control and to improve the steady-state performance, the stator resistance is estimated using an adaptive control scheme. The superiority of the proposed algorithm has been proved by comparison with the conventional SMO through real experiments. This paper consists of five sections, including the introduction. Section II introduces the conventional SMO, and Section III proposes the new SMO. Section IV illustrates the experimental
感应电机无速度传感器直接转矩控制系统的实验研究
华中科技大学文华学院毕业设计（论文）题目：感应电机无速度传感器直接转矩控制系统的实验研究学生姓名：学号：学部（系）：专业年级：指导教师：职称或学位：高级工程师2010 年 5 月 28 日目录目录........................................................................................................................... - 2 - 摘要........................................................................................................................... - 3 - 关键词................................................................................................................ - 3 - Abstract ..................................................................................................................... - 3 - Keywords ........................................................................................................... - 5 - 第1章绪论......................................................................................... - 5 -1.1选题目的及意义：...................................................................................... - 5 -1.2．课题发展现状和前景展望....................................................................... - 5 -1.3 研究内容..................................................................................................... - 6 - 第2章感应电机无速度转矩矢量控制原理......................................................... - 7 -2.1 异步电机的数学模型与坐标变换............................................................. - 7 -2.1.1异步电机的基本方程式.................................................................... - 7 -2.1.2 异步电动机的几种等效电路......................................................... - 10 -2.1.3坐标变换........................................................................................ - 13 -2.2 矢量控制变频调速系统的原理............................................................... - 17 -2.2.1 矢量控制基本方程式..................................................................... - 17 -2.2.2 转差型矢量控制............................................................................. - 19 -2.3 无速度传感器矢量控制系统的结构和速度观测原理........................... - 19 -2.3.1 无速度传感器矢量控制系统的原理............................................. - 19 -2.3.2 感应电机矢量控制系统的基本思路............................................. - 20 -2.3.3转子磁链定向的矢量控制系统...................................................... - 20 -2.4 无速度传感器矢量控制技术................................................................... - 21 - 第3章仿真设计................................................................................. - 23 -3.1 仿真平台................................................................................................... - 23 -3.2 仿真准备................................................................................................... - 24 -3.3 仿真电路................................................................................................... - 25 - 第4章仿真结果................................................................................. - 25 -4.1 仿真结果波形........................................................................................... - 25 -4.2 结果分析................................................................................................... - 26 -4.3结论............................................................................................................ - 27 - 第5章总结......................................................................................... - 27 - 参考文献................................................................................................................. - 27 - 致谢......................................................................................................................... - 29 -摘要直接转矩控制技术是继矢量控制技术之后交流传动领域中一种新兴的控制技术,它省去了复杂的矢量变换,具有动态响应快、结构简单、易于实现等优点。
无刷直流电机转子位置检测技术综述_吴红星
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目前无刷直流电机无位置传感器控制研究的核 心和关键是构架转子位置信号检测线路， 从软、 硬 件两个方面来间接获得可靠的转子位置信号， 以触 发导通相应的功率器件， 驱动电机运转。 近年来， 国内外均出现了很多的位置信号检测方法， 其中较 为成熟的主要有反电动势法、 定子三次谐波法、 续 流二极管法等。 文中总结了无刷直流电机无位置传 感器转子位置的估计方法， 详细论述了各种有效的 检测手段，并针对无刷直流电机无位置传感器控制
通常检测电感值是通过传感器将电感值转变为电信号而由于电机内部本质上是一个非线性系统由于电机漏电抗气隙磁通变化的不确定性对绕组电感的干扰而导致检测的不准确对换向信号的干扰作用十分明显所以这种方法也不是特别常用6卡尔曼滤波法卡尔曼滤波器法的思想是从一组有限的对物体位置的包含噪声的观察序列预测出物体的坐标位置及速度
［1想是： 在传统的反电势法中， 反电势过零点 滞后 30° 为电机的换相点， 而无刷直流电机每隔 60° 换相一次，此法在反电势过零点再经过 90° 后开始换 相，而积分电路恰恰能够满足电流超前电压 90° 的关 系，所以采用积分电路来实现转子磁极位置的检测 ， 由积分器的输出与参考电压进行比较， 从而得到换 向信号，原理如图 5 所示。
0
引
言
传统的永磁无刷直流电动机均需一个附加的位 置传感器，用以向逆变桥提供必要的换向信号。 相 对于无刷直流电机传统的位置传感器， 软件转子位 置辨识技术有着诸多优点， 硬件电路减少， 增强了 电路可靠性， 降低了环境对传感器精度的影响， 减 少连线，减少了电路的干扰。 因此无刷直流电机无 位置传感器慢慢将成为以后无刷直流电机系统的 主流
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（ 2 ） 判定反电势法 国内外也有研究提出了另一种基于反电势法判 定换相时刻的方法
神经网络控制电机
An Approach to Sensorless Operation of the Permanent-Magnet Synchronous Motor Using Diagonally Recurrent Neural NetworksTodd D.Batzel and Kwang Y.Lee,Fellow,IEEEAbstract—Due to the drawbacks associated with the use of rotor position sensors in permanent-magnet synchronous motor (PMSM)drives,there has been significant interest in the so-called rotor position sensorless drive.Rotor position sensorless control of the PMSM typically requires knowledge of the PMSM structure and parameters,which in some situations are not readily available or may be difficult to obtain.Due to this limitation,an alternative approach to rotor position sensorless control of the PMSM using a diagonally recurrent neural network(DRNN)is considered.The DRNN,which captures the dynamic behavior of a system,requires fewer neurons and converges quickly compared to feedforward and fully recurrent neural networks.This makes the DRNN an ideal choice for implementation in a real-time PMSM drive system.A DRNN-based neural observer,whose architecture is based on a successful model-based approach,is designed to perform the rotor position estimation on the PMSM.The advantages of this approach are discussed and experimental results of the proposed system are presented.Index Terms—Motor drives,neural networks,observers,perma-nent magnet motors,sensorless operation.I.I NTRODUCTIONI N high-performance permanent-magnet synchronousmotor(PMSM)applications,high resolution rotor angle information is required to operate the machine efficiently and to generate smooth torque.A resolver or encoder attached to the shaft of the rotor is typically used to supply this position feedback.These high resolution position sensors add length to the machine,raise system cost,increase rotor inertia and require additional cabling.The desire to eliminate the rotor position sensor from PMSM applications has resulted in the development of several techniques for sensorless operation [1]–[6].In general,the techniques for sensorless control of the PMSM include open-loop flux estimators using the stator currents and voltages,third harmonic voltage-based position estimators,back electromotive-force(emf)waveform detection methods,and observers and rotor angle estimators based on position-dependent stator inductance variation.The flux linkage estimation method[1]integrates the phase voltage minus the stator resistance drop to estimate the angle ofManuscript received April12,2002;revised April24,2002.This work was supported in part by the Office of Naval Research under Grant N00014-00-G-0058/007.T.D.Batzel is with the Department of Computer Science and Engineering, Penn State Altoona,Altoona,PA16601USA(e-mail:tdbl20@).K.Y.Lee is with the Department of Electrical Engineering,Pennsylvania State University,University Park,PA16802USA(e-mail:kwanglee@). Digital Object Identifier10.1109/TEC.2002.808386the flux linkage space vector.This angle is then used to produce the appropriate stator current references.This method,how-ever,suffers at low speeds where integrator drift is a problem. Furthermore,estimation accuracy is highly sensitive to varia-tions in the stator resistance,which is known to be temperature dependent.A technique that monitors the third harmonic voltage to ob-tain the rotor angle has been developed in[2].This technique is applicable to the brushless dc machine with trapezoidal back emf waveshape,but is of little relevance to the PMSM,which normally has a sinusoidal airgap flux distribution.In the waveform detection techniques such as in[3],a specific characteristic of the back emf is exploited to determine the rotor position.These methods are simple and may be implemented using low-cost components,but accurate determination of the rotor angle is difficult because of the required low-pass filtering and the low amplitude of the back emf at low speeds.These techniques are more applicable to the brushless dc motor,where an open phase voltage may easily be measured.The pioneering work in[4]implemented a model-based ob-server to determine the rotor angle.This observer,however,was found to be sensitive to mechanical parameters such as load torque,viscous and damping friction and inertia—parameters that are often changing dynamically or are unknown.In[5],the dependency on the mechanical parameters is removed in an ob-server-based approach,but the need for an electrical model of the machine remains.For a salient rotor PMSM application,the position-dependent inductance variation can be monitored to derive the rotor angle [6].This method,however,is unusable for the many machines that are constructed with surface-mounted permanent magnets in the rotor.The increasing role of the artificial neural network(ANN) in a wide variety of engineering applications has spurred in-terest in its application to power electronics and motor drive systems.The artificial neural network(ANN)has several attrac-tive characteristics that justify this interest,including a parallel distributed structure,ability to learn and identify nonlinear dy-namics,ability to generalize and adaptivity.These characteris-tics suggest the enormous potential of the ANN in motor drive systems,including the sensorless PMSM.A frequently used neural-network structure is the feedfor-ward ANN.The drawbacks to using the feedforward ANN in real-time motor control applications include its static mapping characteristic,the requirement for a large number of neurons, and a long training time.Often,a tapped delay line is used with0885-8969/03$17.00©2003IEEEthe feedforward ANN to remove the static mapping restriction and obtain a dynamic mapping.This approach requires that the order of the system dynamics be known in advance to choose a suitable ANN structure.Recently,the diagonally recurrent neural network(DRNN) with dynamic structure was introduced[7]where self-feedback of the hidden neurons ensures that system dynamics may be captured without the tapped delay approach.In addition to its dynamic mapping capabilities,the DRNN requires fewer neurons,is more easily implemented in real-time systems,and converges quickly compared to feedforward and fully recurrent ANN structures[7].Thus,a new approach for neural observa-tion of the PMSM rotor position using a DRNN topology is considered.II.M ODEL-B ASED O BSERVER A PPROACH Assuming that the mathematical model for the PMSM is available,in[5],sensorless control of the PMSM was achieved using the system model and a Luenberger observer.In this work,a separation of time scales is used to yield a linear system model for the PMSM.With this approach,the fast electrical dynamics are representedby(4),and represent the flux linkage,ter-minal voltage,and phase current,respectively,of the fictitiouswindings in the two phase stationary reference frame.Thesymbol(6)(7)Theterm,Fig.1.Rotor position observer for PMSM using Luenberger observer.represent the phase resistance,self-inductance and leakageinductance,respectively,whilematrixindicates that this matrix varies with the angular velocity of therotor and is therefore a time-varying system matrix.From(6),the fast electrical model is dependent on the slowlyvarying angular velocity,which is considered to be a parameterin the fast time scale.In a sensorless application,the angularvelocity must also be estimated.This is accomplished by con-sidering the back emf to be a space vector.From this viewpoint,the magnitude and polarity of the velocity can be determinedfrom magnitude and direction of rotation,respectively,of therotating back emf vector[5]shown in Fig.1is selected to achieve therequired convergence characteristics.A tremendous advantageto this approach is that it does not require any knowledge of themechanical parameters such as load torque,friction,and rotorinertia which are often difficult to obtain,or are changing withtime.Despite the independence from the mechanical variables,thismodel-based approach requires some knowledge of the PMSMstructure and electrical parameters,which in some situations isnot readily available or difficult to obtain.Back EMF waveshapeand saliency characteristics for a PMSM are not always avail-able from the manufacturer.Due to this potential dilemma,anew approach should be considered—the neural observer.Dueto the many advantages of the approach outlined before,theDRNN-based neural observer is based in principal on the struc-ture of the Luenberger observer developed in[5].Fig.2.DRNN structure.III.D IAGONAL R ECURRENT N EURAL N ETWORKA.DRNN StructureThe structure of the DRNN is shown in Fig.2for a system with recurrent neurons,and two output neurons.In a DRNN,the only recurrent connections that are allowed are self-recurrent connections in the hidden layer,where the re-current connections are assumed to incorporate a delay.In the absence of the self-recurrent connections,the architecture be-comes a feedforward network.The generated output of the m th output layer neuron and j th recurrent layer neuron are given in(9)and(10),respectively, and the sum of inputs to recurrent neurons is given in(11)(10),)in(10)is the commonly used bipolar tansigmoid transfer charac-teristicwhereand in(9)whilein(11).B.Dynamic BackpropagationThe goal of backpropagation is to minimize a cost function,which is normally selected to be the squared error between theactual ANN output and the desired value.Let the training setconsist of the desired output oftheis representedby(13)and the total cost function to be reduced during the trainingprocessisrepresents the learningrate,is a momentum term,and.The remaining results required to implement dynamic back-propagation are summarized in(16)–(20)(16)(19)Fig.3.DRNN-based neural observer for PMSM rotor angle.process shown in Fig.3is separated into stator current,angular velocity,and rotor position estimators.A.Description of Neural-Based Rotor Angle Observer 1)Neural Current Observer:The neural current observer included in the structure of Fig.3is used to map the estimatedangular velocity and the applied terminalvoltageto the estimated statorcurrent(21)where),as shown in Fig.4.Thus,theDRNN is trained to estimate the statorcurrent(22)whereused in the model-basedapproach,which correctsstatesFig.5.Rotor angle estimation and correction block.Comparing(22)and(24)and assuming a small angular dif-ference(25)whereand are the current estimation errors defined in(23).Clearly,from(25),information regarding the angle esti-mation error is included in the current estimation error quanti-ties,which suggests the position estimation strategy shown inFig.5.B.DRNN Topology for Rotor Position ObserverThrough experimentation,15neurons in the hidden recurrentlayer were found to produce good results for the neural currentobserver.This number was selected to produce fast convergenceand robust dynamic capturing capabilities.The output layer con-tains two neurons—one for each of the direct and quadratureaxis current estimates.Both the velocity and position estimation DRNN use five neu-rons in the hidden layer.In both cases,the output layer consistsof a single neuron.The recurrent and output layers neurons foreach of the three DRNN estimators use a tansigmoid and lineartransfer functions,respectively.C.DRNN Observer TrainingThe goal of the DRNN training process is to minimize therotor position estimation error and to learn the machine dy-namics.The proposed DRNN-based system was trained in twosteps:offline training in which the dynamics of the PMSM arelearned and online training where the adaptation of PMSM pa-rameters is achieved.1)Offline Training:The offline training process starts witha random initialization of the weights in each layer.Weights arenormalized based on the expected range at the input neurons.The first set of training data consists of steady-state motoringin each direction with a constant load,in which case the rotorvelocity is the constant synchronous speed.For each trainingiteration,the DRNN output is compared to its target.The re-sulting error function is then used with the dynamic backprop-agation algorithm to adjust the weights of the DRNN.After thesum-squared error for the first set of training data converges to apredetermined threshold,additional training data consisting ofvarious speeds and load torques are slowly introduced one at atime.The offline training process is completed when the trainingdata set consists of the entire range of operation for the motorunder consideration and the sum-squared error meets the goal.Each of the three DRNN neural estimators was first trainedoffline by using dynamic backpropagation.The neural currentand velocity estimators are first trained,followed by the posi-tion estimator.The input vector to the current estimator DRNNconsists of the stator voltages and rotor angular velocity,whilethe target vector is the actual stator current vector.The velocityestimation DRNN uses the stator voltage and current vectors asthe inputs and the actual rotor velocity as the target vector.Theposition estimation DRNN uses the rotor velocity and currentestimation errors as its inputs and the actual rotor angle as thetarget.For the offline training,the actual rotor angle and velocitywere both obtained from a hollow shaft encoder temporarily at-tached to the shaft of the PMSM.For the current estimator DRNN,approximately6500training iterations at a constant learning rate of.05producedthe weights used to perform system simulations.Significantlyfewer iterations were required by both the velocity and positionestimation DRNN.2)Online Training:Much like state feedback correction isnecessary in the model-based approach,adaptive correction ofthe DRNN weights is important in the neural estimator.Onlinetraining is performed to account for the inevitable parameterdrift associated with operating temperature fluctuations.For the DRNN current estimator,adaptive correction isachieved much like the offline training,with the exception thatestimated rotor velocity is used as an input to the DRNN sinceactual velocity is not available in the real system.The velocityestimator may be trained online during periods of steady-stateoperation.In steady state,the time derivative of the estimatedrotor angle approaches the actual velocity over a sufficientperiod of time.Thus,under steady-state operating conditions,the actual velocity determined from the estimated rotor anglecan be used to adjust the weights of the velocity estimatoronline.Online training of the position estimation DRNN wasnot used,since the actual rotor angle is not available in asensorless system.D.Experimental ResultsAfter the training of the DRNN,some test patterns were ap-plied to the system to evaluate the effectiveness of the trainingprocess.The performance is tested under various steady-stateand transient conditions.Fig.6shows the DRNN observer performance at relativelyhigh speed with a load equal to half-rated torque.In this exper-iment,an initial angle estimation error of30electrical degreeswas intentionally introduced to the system.As demonstrated inthe figure,the error is quickly corrected and the angle estima-tion error quickly approaches zero.In Fig.7,the robustness of the system to load torque vari-ations is examined.For this experiment,a fast load change isimposed on the system.The results of this experiment demon-strate that the proposed system performs well under such loadchanges.This is the expected result,since the neural observeris patterned after the model-based approach,which was shownFig.6.NN estimation accuracy at 800r/min,5ft-lb.Fig.7.NN estimation accuracy—400r/min,fast loadchange.Fig.8.Online training of 2.5-HP PMSM—initial training iteration.in [5]to be robust to the mechanical parameters such as load torque.In order to evaluate the effectiveness of the DRNN adapta-tion process,the system was trained offline using data from a 7.5horsepower PMSM with stator resistance and inductance of.12and 1.3H,respectively,and50percent error in the estimatedrotor velocity was intentionally introduced to the system.TheFig.9.Online training of 2.5-HP PMSM—after many trainingiterations.Fig.10.NN estimation accuracy with velocty estimationerror.Fig.11.NN estimation accuracy for speed reversal.figure shows that,despite the exaggerated error in the angular velocity,the rotor position is still estimated with acceptable ac-curacy for many applications.Finally,Fig.11shows the performance of the rotor position estimator during a speed reversal.Here,significant error is ev-ident around the zero speed operation where the slope of therotor angle becomes zero.This is due to the well-known un-observability of rotor angle at zero speed because of the lack of developed stator voltage under those operating conditions. Sensorless operation at zero speed is known to be problematic, and thus,startup and speed reversals are often achieved through alternate strategies[10]–[13]while the estimator is effectively disabled.V.C ONCLUSIONSA neural-based observer,whose architecture is patterned from the results of a model-based approach,was designed and applied to the rotor position estimation task for the PMSM. The DRNN was selected for the neural topology due to its dynamic mapping characteristics,fast convergence,and ease of implementation in real-time systems.Both the DRNN and model-based approach have demonstrated insensitivity to mechanical parameters such as load torque,inertia,and friction.In addition,the DRNN approach has shown the ability to learn the PMSM dynamics and robustness to the unavoidable drift and uncertainty of PMSM parameters.The advantages demonstrated by the DRNN approach are significant in gen-eral-purpose PMSM drives,where the end user may not know the PMSM parameters required by a model-based approach. In cases where machine parameters,saliency characteristics, and back emf waveshape are well defined,however,the model-based approach has produced superior rotor position estimation results in the experiments.R EFERENCES[1]R.Wu and G.Slemon,“A permanent magnet motor drive without a shaftsensor,”IEEE Trans.Ind.Applicat.,vol.27,pp.1005–1011,Sept./Oct.1991.[2]J.Moreira,“Indirect sensing for rotor flux position of permanent magnetAC motors over a wide speed range,”IEEE Trans.Ind.Applicat.,vol.32,pp.1394–1401,Dec.1996.[3]S.Ogasawara,K.Suzuki,and H.Akagi,“A sensorless brushless DCmotor system,”Electrical Engineering in Japan,vol.12,no.5,pp.109–117,1992.[4]L.Jones and ng,“A state observer for the permanent magnet syn-chronous motor,”IEEE Trans.Ind.Electron.,vol.36,pp.374–382,Aug.1989.[5]T.D.Batzel and K.Y.Lee,“Slotless permanent magnet synchronousmotor operation without a high resolution rotor angle sensor,”IEEE Trans.Energy Conversion,vol.15,pp.366–371,Dec.2000.[6] A.Kulkarni and M.Ehsani,“A novel position sensor eliminationtechnique for the interior permanent magnet synchronous motor drive,”IEEE Trans.Ind.Applicat.,vol.28,pp.144–150,Feb.1992.[7] C.C.Ku and K.Y.Lee,“System identification and control using diag-onal recurrent neural networks,”in Proc.1992Amer.Control Conf.,vol.I,June1992,pp.545–549.[8],“Diagonal recurrent neural networks for dynamic systems con-trol,”IEEE Trans.Neural Networks,vol.6,pp.144–156,Jan.1995.[9]R.H.Park,“Two reaction theory of synchronous machines—Part I,”AIEE Transactions,vol.48,no.2,pp.716–730,1929.[10]M.Schroedl,“Sensorless control of AC machines at low speed andstandstill,”in Conf.Rec.—Ind.Applicat.Soc.Meeting,vol.1,1996,pp.270–277.[11]N.Matsui and T.Takeshita,“A novel starting method of sensorlesssalient-pole brushless motor,”Proc.Conf.Rec.1994Ind.Applicat.Soc.Annu.Meeting,vol.1,pp.386–392,Oct.1994.[12]T.Aihara,A.Toba,and T.Yanase,“Sensorless torque control ofsalient-pole synchronous motor at zero speed operation,”in IEEE Appl.Power Electron.Conf.Exposition,vol.2,1997,pp.715–720.[13]T.D.Batzel and K.Y.Lee,“Starting method for sensorless operationof slotless permanent magnet synchronous machines,”in Proc.IEEE Power Eng.Soc.Summer Meeting,vol.2,July1999,pp.1243–1247.Todd D.Batzel received the B.S.and Ph.D.degreesin electrical engineering from the PennsylvaniaState University,University Park,in1984and2000,respectively,and the M.S.degree in electricalengineering from the University of Pittsburgh,Pittsburgh,PA,in1989.Currently,he is Assistant Professor of computerscience at the Pennsylvania State University,Altoona.His research interests include machinecontrols,electric drives,power electronics,andartificial intelligence applications to control and embedded controlsystems.Kwang Y.Lee(F’01)received the B.S.degree inelectrical engineering from Seoul National Univer-sity,Korea,in1964,the M.S.degree in electricalengineering from North Dakota State,Fargo,in1968,and the Ph.D.degree in systems science fromMichigan State University,East Lansing,in1971.Currently,he is a Professor of electrical engi-neering and is Director of Power Systems ControlLaboratory at Pennsylvania State University,Uni-versity Park.He has also been with Michigan State,Oregon State,and the University of Houston.His interests include power systems operation and planning,expert systems,and intelligent system applications to power systems.Dr.Lee is an Editor of IEEE TRANSACTIONS ON NEURAL NETWORKS and IEEE TRANSACTIONS ON ENERGY CONVERSION.Dr.Lee is a Fellow of the IEEE.。
Implementation of sensorless vector control for super-high-speed PMSM of turbo-compressor
Implementation of Sensorless Vector Control for Super-High-Speed PMSM of Turbo-Compressor Bon-Ho Bae,Member,IEEE,Seung-Ki Sul,Fellow,IEEE,Jeong-Hyeck Kwon,Member,IEEE,and Ji-Seob ByeonAbstract—This paper describes the implementation of twovector control schemes for a variable-speed131-kW perma-nent-magnet synchronous motor drive in super-high-speedapplications.The vector control with a synchronous referenceframe current regulator was implemented with challengingrequirements such as an extremely low stator inductance(28Fig.3.Power circuit diagram of the proposed super-high-speed PMSM drive.and the operation of the current regulator was tested up toan excitation frequency of1200Hz.In addition,the vectorcontrol schemes with and without the discrete Hall sensors areproposed.In the case of a vector control with three discreteHall sensors,the discrete Hall sensors provide rough positioninformation with a resolution ofs.Becausethe general-purpose microprocessors cannot meet the requiredcalculation time,the TMS320VC33-150digital-signal-pro-cessor(DSP)-based digital controller was developed for theimplementation.The experimental data showed that it takesless than20BAE et al.:SENSORLESS VECTOR CONTROL FOR SUPER-HIGH-SPEED PMSM OF TURBO-COMPRESSOR813Fig.4.Block diagram of the synchronous reference frame current regulator with the inductor.(a)(b)(c)Fig.5.Current waveform of synchronous reference frame current regulator with an inductor load.(a)Current waveforms with excitation frequency of20Hz.(b)Current waveforms with excitation frequency of1200Hz.(c)Current waveforms with excitation frequency of1200Hz.(Magnitude of current reference is changed from350to200As,which is relatively large consid-ering the short PWM update period,33.33814IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS,VOL.39,NO.3,MAY/JUNE2003 Fig.5shows the experimental results with the proposed cur-rent regulator.A three-phase air-core reactor is used for the test,and the inductance is set to the same value of the stator induc-tance of the PMSM,28,-phasecurrent measured by the current probe set(Tektronix CT-4,A6302and AM503),.The traces shown in Fig.5(a)present the experimental results using an excitation frequencyof20Hz.From the results shown in Fig.5(a),it can be seenthat the performance of the current regulator was degraded atthe zero crossings of the phase currents because of the effectof the dead time and the zero-current clamping even aftercareful compensation[7],[8],[10].The traces in Fig.5(b)show the experimental results with an excitation frequency of1200Hz.Because the dead-time effect and the zero-currentclamping effect are reduced at high frequency,the currents arewell controlled sinusoidally without degradation.However,themeasured currents in Fig.5(a)and(b)show large ripplesdue to the very small load inductance.The delay in the sampledcurrentto200A.The bottom trace in Fig.5(c)shows the magnitude of thecurrent vector,,30from the input of the latestangle pulse to the sampling point was measured by the pro-grammable logic device.The speed of the motor,BAE et al.:SENSORLESS VECTOR CONTROL FOR SUPER-HIGH-SPEED PMSM OF TURBO-COMPRESSOR815Fig.9.Block diagram of sensorless vector control scheme without position sensor.Based on the assumption that the rotor speed does not change between the pulses,the rotor angle at the samplingpoint s at thenormal speed and the speed does not change rapidly,reasonably accurate speed information can be calculated by the algorithm.The test results with the proposed vector control scheme using the angle information of the Hall-effect sensors are shown in Fig.7.For the experiment,three discrete Hall-effect sensors with a sensing magnet were installed in the PMSM and a special digital logic circuit was implemented to measuretheangleof Fig.8using the programmable logic device.From top to bottom,the traces showthe-axis current ,the motor phasecurrent-axis current are shown.For the test,the extrapolation of the rotor angle has not been carried out at low speed,where the speed information is not reliable.Therefore,the discontinuous angle information deteriorated the performance of the current regulator.Fig.7(b)shows the acceleration from 17000to 20000r/min.Because precise angle information is available using the extrapolation,the vector control scheme provides the effective current and speed regulation.Compared to the conventional position sensors,the discrete Hall sensors were much more reliable in a super-high-speed op-eration.However,the installation of the Hall sensors and the sensing magnet limits the mechanical design,and the sensorless control is a better solution for super-high-speed applications.In the case of the turbo-compressor application,the installation of a sensing magnet even causes difficulties in the aerodynamic design.V .S ENSORLESS V ECTOR C ONTROL S CHEME W ITHOUTANY P OSITION S ENSOR Fig.9shows a block diagram of the sensorless vector control scheme without the position sensor.In the diagram,the feedfor-wardtermsis the error between the real rotorangle-axis voltageerror,which has to be compensated for bytheis small,the output voltage ofthe(11)In the proposed estimator in Fig.9,the error signal of (11)is processed by the PI compensator to derive the rotor speed and the rotor angle is calculated by integrating the estimated speed.In the conventional method [3],a differentiation process816IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS,VOL.39,NO.3,MAY/JUNE2003Fig.10.Frequency pattern for constant current control with pre-patternedfrequency.is used to calculate the speed but this makes the system vul-nerable to measurement noise.The experimental study revealsthat the proposed estimator provides a very accurate and robustspeed information for the application.However,at the zero andlow speed,the back-EMF voltage is not high enough for theproposed vector control.Hence,for the initial alignment andstarting from zero speed,the current was controlled with a con-stant magnitude using a pre-patterned angular frequency.In ad-dition,the angle for the synchronous reference frame was calcu-lated by integrating the frequency.Fig.10shows the frequencypattern for the initial alignment and starting.As shown in step Iof Fig.10,the initial value of the frequency pattern is set to asmall but constant speed for the initial rotor alignment.After the alignment,according to the speed pattern in step II,themotor is accelerated up to the threshold speed.Over thethreshold speed,the motor is then controlled by the proposedsensorless control and the speed is estimated by the proposedestimator shown in Fig.9.VI.E XPERIMENTAL R ESULTS W ITH S ENSORLESS C ONTROLThe experimental results with the proposed sensorless con-trol are shown in Figs.11–14.Fig.11shows the starting char-acteristics from zero speed to20000r/min.The PMSM wasaccelerated by the rotating current vector with the precalcu-lated frequency pattern shown in Fig.10.In order to align therotor,the current vector was rotated with the starting frequency-axis current tracks the command with a smallripple current,which is caused by the dead time and zero-currentclamping effects.Because the current control bandwidth was setlow for the sensorless algorithm,the actual current tracks thecommand with a delay.The bottom trace shows the measuredphase current,,which was measured by a current probeset(Tektronix AP504CX and AM503B).Due to the extremelysmall stator inductance,the fundamental current was accompa-nied by a significant ripple current,which was caused by the15-kHz PWM switching.Fig.13(b)shows the magnified wave-forms of the last two periods in Fig.13(a).The traces show thatthe phase current is controlled sinusoidally by the precise sam-pling of the fluctuating current.In Fig.13,the measured phase current shows a rela-tively large current ripple,which can increase the temperatureof the rotor.Because a high temperature can degrade theBAE et al.:SENSORLESS VECTOR CONTROL FOR SUPER-HIGH-SPEED PMSM OF TURBO-COMPRESSOR817(a)(b)Fig.13.Current waveforms with the proposed sensorless control (acceleration from 58000to 60000r/min).(a)Current waveforms.(b)Magnified figures of the last two periods of current waveforms in (a).mechanical stability of the rotor,it is desirable to reduce the cur-rent ripple.Fig.14shows the current waveforms with a28-,shows that the current ripple is reducedremarkably by the external inductor.As shown in Fig.14,the current ripple can be easily reduced by the external inductor.However,because the adoption of an external inductor requires a higher inverter output voltage,the value of the inductor can be determined by a tradeoff between the current waveform and the inverter output voltage capability.VII.C ONCLUSIONThe development of a super-high-speed drive for a 131-kW 70000-r/min PMSM for use in a turbo-compressor wasdis-(a)(b)Fig.14.Current waveforms with an external inductor of 28 H (at speed of 65000r/min).(a)Current waveforms.(b)Magnified waveforms.cussed.The synchronous reference frame current regulator was implemented with challenging requirements such as a low stator inductance(28818IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS,VOL.39,NO.3,MAY/JUNE2003[2]W.L.Soong,G.B.Klima,R.N.Johnson,R.A.White,and ler,“Novel high-speed induction motor for a commercial centrifugal com-pressor,”IEEE Trans.Ind.Applicat.,vol.36,pp.706–713,May/June2000.[3]L.Xu and C.Wang,“Implementation and experimental investigationof sensorless control schemes for PMSM in super-high variable speedoperation,”in Conf.Rec.IEEE-IAS Annu.Meeting,vol.1,1998,pp.483–489.[4]M.Mekhiche,J.L.Kirtley,M.Tolikas,E.Ognibene,J.Kiley,E.Hol-mansky,and F.Nimblett,“High speed motor drive development for in-dustrial applications,”in Conf.Rec.IEMD’99,1999,pp.244–248.[5]T.R.Rowan and R.L.Kerkman,“A new synchronous current regulatorand an analysis of current-regulated PWM inverter,”IEEE Trans.Ind.Applicat.,vol.22,pp.678–690,July/Aug.1986.[6] D.W.Novotony and T.A.Lipo,Vector Control and Dynamics of ACDrives.New York:Oxford Univ.Press,1996.[7]J.-W.Choi and S.-K.Sul,“Inverter output voltage synthesis using noveldead time compensation,”IEEE Trans.Power.Electron.,vol.11,pp.221–227,Mar.1996.[8]J.W.Choi and S.K.Sul,“New dead time compensation eliminating zerocurrent clamping in voltage-fed PEM inverter,”in Conf.Rec.IEEE-IASAnnu.Meeting,1994,pp.977–984.[9]V.Blasko,V.Kaura,and W.Niewiadomski,“Sampling of discontinuousvoltage and current signals in electrical drives:A system approach,”IEEE Trans.Ind.Applicat.,vol.34,pp.1123–1130,Sept./Oct.1998.[10]S.-H.Song,J.-W.Choi,and S.-K.Sul,“Digitally controlled AC drives,”IEEE Ind.Applicat.Mag.,vol.6,pp.51–62,July/Aug.2000.[11]T.Ohmae et al.,“A microprocessor-controlled high accuracywide-range speed regulator for motor drives,”IEEE Trans.Ind.Electron.,vol.29,pp.207–211,Aug.1982.Bon-Ho Bae(S’99–M’03)was born in Korea in1966.He received the Ph.D.degree in electricalengineering from Seoul National University,Seoul,Korea,in2002.He joined Rotem Company(formerly DaewooHeavy Industries Ltd.)in1992and worked for eightyears on the development of propulsion systems forelectric trains.His recent research projects were thedevelopment of the1.2-MV A IGBT inverter for thetraction system of a subway train,sensorless vectordrive system with130-kW70000-r/min PMSM for turbo-compressor,and the42-V ISG system using the high-saliency-ratio IPMSM.He is currently with the General Motors Corporation Advanced Technology Center,Torrance,CA,and his research interests are electric machine drives and automotiveapplications.Seung-Ki Sul(S’78–M’87–SM’98–F’00)was bornin Korea in1958.He received the B.S.,M.S.,andPh.D.degrees in electrical engineering from SeoulNational University,Seoul,Korea,in1980,1983,and1986,respectively.From1986to1988,he was an Associate Re-searcher with the Department of Electrical andComputer Engineering,University of Wisconsin,Madison.He then was with Gold-Star IndustrialSystems Company as a Principal Research Engineerfrom1988to1990.Since1991,he has been a member of the faculty of the School of Electrical Engineering,Seoul National University,where he is currently a Professor.His current research interests are power electronic control of electric machines,electric vehicle drives,and power convertercircuits.Jeong-Hyeck Kwon(S’96–M’99)received theB.S.degree in electronic engineering in1996fromYeungnam University,Taegu,Korea,and the M.S.degree in electronic engineering in1999fromChangwon National University,Changwon,Korea,where he is currently working toward the Ph.D.degree.Since1996,he has been a Researcher in indus-trial control in the Department of Electronics,PowerSystem R&D Center,Samsung Techwin Company,Changwon,Korea.His current research is focused onapplied super-high-speed motors anddrivers.Ji-Seob Byeon received the B.S.and M.S.degreesin electronic engineering from Changwon NationalUniversity,Changwon,Korea,in1998and2001,respectively.Since2001,he has been a Researcher in indus-trial control in the Department of Electronics,PowerSystem R&D Center,Samsung Techwin Company,Changwon,Korea.His current research is focused onDSP applications and digital control.。
介绍自动驾驶技术的英语作文
介绍自动驾驶技术的英语作文In the rapidly advancing technological era, autonomous driving, also known as self-driving or driverless technology, has emerged as a potential revolution in the automotive industry. This innovative technology aims to revolutionize transportation by eliminating the need for human drivers and enhancing safety, efficiency, and convenience.The concept of autonomous driving is not new, but it has gained significant momentum in recent years, thanks to advancements in areas such as sensor technology, artificial intelligence (AI), and machine learning. These technologies enable vehicles to perceive their environment, make decisions, and navigate roads autonomously, without the need for human intervention.One of the most significant benefits of autonomous driving is the potential to reduce road accidents. Human error is a leading cause of most road accidents, and autonomous vehicles are designed to eliminate this factor. By using a combination of sensors, cameras, radars, and AI algorithms, these vehicles can detect and respond topotential hazards more quickly and accurately than human drivers.Another benefit of autonomous driving is the improvement in traffic flow and congestion. Autonomous vehicles can communicate with each other and infrastructure, allowing them to coordinate their movements and optimize traffic flow. This can lead to smoother, more efficient traffic patterns and significantly reduce congestion.Autonomous vehicles also offer greater convenience and accessibility. Users can travel without the need to focuson driving, opening up the possibility for more productiveor enjoyable travel time. This could have significant implications for individuals with disabilities or those who cannot drive, as it would provide them with greater independence and mobility.However, while the potential benefits of autonomous driving are vast, there are also numerous challenges and ethical considerations that need to be addressed. Safety is a critical concern, and ensuring that autonomous vehicles can handle all possible scenarios and emergencies is essential. Additionally, there are legal and regulatoryissues that need to be resolved, as well as concerns about data privacy and security.Despite these challenges, the future of autonomous driving looks bright. As technology continues to improveand more companies invest in research and development, we can expect to see autonomous vehicles become increasingly common on our roads. The potential benefits of safer, more efficient, and more convenient transportation are too great to ignore, and as these vehicles become more widely adopted, they will likely transform the way we travel.**自动驾驶技术的未来展望**在科技飞速发展的时代，自动驾驶技术，也被称为无人驾驶或自动驾驶技术，已成为汽车行业的一场潜在革命。
维京智能传感器RS-SSD V3.0用户手册说明书
Viking Smart Sensor RS-SSD ManualVersion 3.0Manual version 1.2Page 2 of 8Thank you for the purchase of the Viking Smart Sensor RS-SSD V3.0. This advanced sensor will provide the user with better access to the Viking ’s many functions that will improve the user experience.The new Smart Sensor is compatible with all Viking firmware versions, however many of the new functions detailed below will only be compatible on Vikings with firmware version 4.0 or above. This sensor is fully backwards compatible with older SMT-920 Viking firmware and can be used as a replacement sensor.Sensor FunctionsThe RS-SSD temperature and humidity sensor has been designed to permit the user to see and control room temperature. Further, if enabled by the installer, the RD-SSD version 3.0 will permit the control of several other functions such as setpoint adjustments or the ability to turn the air conditioning on or off.Turning the Air Conditioning On or OffIf enabled by the installer, this function will permit you to force the air conditioning off, overriding the SMT-920 Viking time clock function or when youwish to control the air conditioning manually. To force the air conditioningoff, press and hold the Mode Select button for 2 seconds.Page 3 of 8Using the After Hours Run FunctionIf enabled by the installer, this function will permit you to start the after hours run timer so your air conditioning can be operated outside of normal operating hours for a pre-set time with theAuto Off timer. The After-Hours function is only available outside of normal operating hours. To use the after hours run function, simply tap the Mode Select button when the text “A fter Hours ” is shown.Adjusting Heating and Cooling ModeIf enabled by the installer, you will be permitted to select Heating Only mode where no cooling will becalled, Cooling Only mode where no heating will be called or Auto mode where the heating and cooling will be controlled. Simply tap the Mode Select button to toggle through all available modes.Adjusting Set TemperatureIf enabled by the installer, you will be permitted to adjust the set temperature by tapping the temperature Up/Down buttons for the available heating or cooling modes. These setpoints shown on the RS-SSD version 3.0 sensor adjust the setpoints stored in the SMT-920 Viking controller. If Single Setpoint adjustment mode is enabled, then the setpoint adjustment will apply equally to both the heating and cooling setpoints that have been set in the SMT-920 Viking.Fan Mode AdjustmentIf enabled by the installer, you will be permitted to adjust the mode of the air conditioning Fan.Available modes will be shown and adjustable by tapping the Fan button.Page 4 of 8Installing the SensorThe wiring overview shown (below) indicates how each sensor in the network of sensors is wired. S1, S2, S3 & S4 on the sensor is wired to S1, S2, S3 & S4 on the SMT-920 Viking. Each sensor is fitted with a set of switcheson the printed circuit board as shown in the diagram below. These switches set the address of the sensor . It doesn’t matter what address you select but no two sensors connected to any one SMT-920 Viking should share the same network address.Do not set all DIP switches ONUp to 15 Smart Sensors can be installed on to any single SMT-920 Viking. Theindividual Smart Sensor will show its local temperature and humidity however the SMT-920 Viking will control to the average temperature of all 16 sensors. Further, Hi or Low select can be used if set by the installer within the Viking installer menu.Page 5 of 8Remote Temperature SensorThe RS-SSD room temperature sensor can be disabled and a standard Smart Temp 2 wire remote sensor (RS-01/02) can be used should you wish to measure room temperature at a different location to the RS-SSD version 3.0 location. Wire the RS-01/02 into the RS-SSD “TT” terminals .Do not enable Display Only mode if you intend to use 2 wire remote sensorsconnected to the RS-SSD.Installer Options MenuThe RS-SSD has an Installer Optionsmenu that permits you to control or adjust various sensor functions.To Enter the Installer MenuPress and hold the lower right corner of the LCD until the display changes and shows you the number “15". Use the Up/Down button to adjust the display to show “21", the factory default PIN or your previously set PIN if changed from the factory default.Tap the lower right corner to enter the Installer menu. If the PIN is correct you will enter the menu. If you enter an incorrect PIN you will be exited fromthis menu.Tap the lower right corner to advance through the menu options, tap the lower left corner to step backwards through the menu. Tap the arrow pointing up on the left of the display to exit the Installer menu and use the Up/Down buttons on the right of the display to adjust the menu options.Installer Options MenuPage 6 of 8Page 7 of 8Smart Temp Australia P/LUnit 20 /1488 Ferntree Gully RdKnoxfield VictoriaAustralia 3180+61 397630094.au*********************.auCopyright Smart Temp Australia P/L 2020 Page 8 of 8。
太赫兹超分辨成像 电池
太赫兹超分辨成像电池下载温馨提示：该文档是我店铺精心编制而成，希望大家下载以后，能够帮助大家解决实际的问题。
文档下载后可定制随意修改，请根据实际需要进行相应的调整和使用，谢谢!并且，本店铺为大家提供各种各样类型的实用资料，如教育随笔、日记赏析、句子摘抄、古诗大全、经典美文、话题作文、工作总结、词语解析、文案摘录、其他资料等等，如想了解不同资料格式和写法，敬请关注!Download tips: This document is carefully compiled by the editor. I hope that after you download them, they can help yousolve practical problems. The document can be customized and modified after downloading, please adjust and use it according to actual needs, thank you!In addition, our shop provides you with various types of practical materials, such as educational essays, diary appreciation, sentence excerpts, ancient poems, classic articles, topic composition, work summary, word parsing, copy excerpts,other materials and so on, want to know different data formats and writing methods, please pay attention!太赫兹技术作为一种新兴的成像技术，具有超越传统光学成像分辨率的优势，因此在电池领域的应用备受关注。
丰田TSS-3.0（Toyota Safety Sense 3.0）说明书
Pre-Collision System,2 or PCS, is designed to help drivers mitigate or avoid frontal collisions by detecting a vehicle, pedestrian, or bicyclist and providing an audio and/or visual forward collision warning and brake assist under certain circumstances.Enhanced intersection support with improved detection range capability, including (in certain circumstances), oncoming vehicles in more than one lane while turning and vehicles approaching from a lateral directionAlong with a vehicle, a bicyclist, or a pedestrian, now capable ofdetecting a motorcyclist in certain circumstancesTOYOTA SAFETY SENSE ™ 3.0Lane Departure Alert,3 or LDA, is designed to detect inadvertent lane departure at speeds above 30 miles per hour and issue an audio and visual warning. If the driver does not take corrective action, the system will provide gentle corrective steering to help keep the vehicle in the lane.Lane Departure Alert now provides enhanced lane recognition to detect certain three-dimensional objects used to define the lane, like certain types of guard railsToyota Safety Sense ™,1 or TSS, is a suite of active safety technologies and advanced driver assistance systems. Toyota Safety Sense™3.0 (TSS 3.0) introduces several enhancements over the previous generation, including an upgraded forward-facing camera with higher resolution and wider angles, and an improved radar sensor for a longer and wider field of view.As a result, these updates help enhance some of the features that make up TSS 3.0.Additionally, this latest safety suite includes the capability of over-the-air (OTA) software updates to TSS 3.0 systems on certain vehicles that can bring future improvements and functionalities, without needing a trip to the dealer.Here is a quick look at TSS 3.0’s systems and what’s new.PRE-COLLISION SYSTEMLANE DEPARTURE ALERTPLAY VIDEOWHAT’S NEWWHAT’S NEWLANE TRACING ASSISTLane Tracing Assist,5 or LTA, is designed to help the driver keep the vehicle centered in its lane. LTA functions when DRCC is activated, and detects lane markings, as well as the path of the vehicle ahead, and is designed to actively provide steering inputs that help keep the vehicle centered in its lane.Capable of steering the vehicle within its lane to offset the vehicle’s driving path to help provide more space between objects being passed in an adjacent laneEmergency Driving Stop System (EDSS)6 on vehicles equipped with a driver monitor camera is designed to also confirm the driver’s eyes are attentive to the road aheadIf the system determines the driver is not attentive, and the driver does not respond to prompts to resume control of the vehicle, itcan bring the vehicle to a stop under certain conditionsWHAT’S NEWDYNAMIC RADAR CRUISE CONTROLDynamic Radar Cruise Control,4 or DRCC, is an adaptive cruise control system that uses vehicle-to-vehicle distance control to help maintain a preset distance from the vehicle ahead of the driver at cruising speeds set above 20 miles per hour.Update from three to four cruise distance settingsEnhanced vehicle detection that enables the system to help provide smoother, more natural speed adjustments WHAT’S NEWROAD SIGN ASSISTRoad Sign Assist,7 or RSA, is designed to help detect speed limit signs, stop signs, do not enter signs, and yield signs, and display an icon of the sign on the vehicle’s Multi-Information Display. The system is designed to help provide the driver with additional awareness of posted road signs and can also provide alerts, like if the vehicle’s speed exceeds the posted speed limit.RSA is now capable of detecting a wider variety of road signs, suchas warning signs like pedestrian crossing WHAT’S NEWAUTOMATIC HIGH BEAMSAutomatic High Beams,8 or AHB, are designed to help drivers see more clearly at night, while also reducing glare for surrounding drivers.DISCLOSURES1. Toyota Safety Sense effectiveness depends on many factors including road, weather and vehicle conditions. Drivers are responsible for their own safe driving. Always pay attention to your surroundings and drive safely. See Owner’s Manual for limitations.2. The Pre-Collision System (PCS) with Pedestrian Detection (PD) is designed to help reduce the crash speed and damage in certain frontal collisions involving a vehicle, a pedestrian, bicyclist or motorcyclist. PCS w/PD is not a substitute for safe and attentive driving. System effectiveness depends on many factors, such as speed, size and position of vehicle, pedestrian, bicyclist or motorcyclist and weather, light and road conditions. See Owner’s Manual for limitations.3. Lane Departure Alert with Steering Assist is designed to read visible lane markers under certain conditions. It provides a visual/audible alert and slight steering force when lane departure is detected. It is not a collision-avoidance system or substitute for safe and attentive driving. Effectiveness depends on many factors including road, weather and vehicle conditions. See Owner’s Manual for limitations.4. Dynamic Radar Cruise Control is not a substitute for safe and attentive driving. See Owner’s Manual for instructions and limitations.5. The Lane Tracing Assist (LTA) lane centering function is designed to read visible lane markers and detect other vehicles under certain conditions. It is only operational when DRCC is engaged. Not available on vehicles with manual transmissions. See Owner’s Manual for limitations.6. Emergency Driving Stop System will not detect all emergency situations and only operates when Dynamic Radar Cruise Control and Lane Tracing Assist are active. See Owner’s Manual for additional limitations.7. Road Sign Assist only recognizes certain road signs. See Owner’s Manual for limitations.8. Automatic high beams operate at speeds above 25 mph. See Owner’s Manual for instructions and limitations.9. Proactive Driving Assist (PDA) is designed to detect certain objects or curves in the road and provide gentle braking and/or steering support. PDA is not a substitute for safe and attentive driving. System effectiveness depends on many factors, such as speed, size and position of detected objects and weather, light and road conditions. See Owner’s Manual for additional limitations and details.Published 01.18.23Proactive Driving Assist,9 or PDA, is an all-new feature on certain vehicles equipped with TSS 3.0. When conditions are met, PDA can provide gentle braking when driving into curves or gentle braking and/or steering to help support driving tasks, such as distance control between the driver’s vehicle and a preceding vehicle, pedestrian, or bicyclist.The PDA feature set includes Obstacle Anticipation Assist , which is designed to detect vehicles parked on the side of the road, or pedestrians or bicyclists either on the side of the road or crossing the roadPDA also features Deceleration Assist , which is designed to provide gentle braking to gradually reduce vehicle speed when the system detects preceding vehicles, motorcycles, or certain upcoming curves in the roadSteering Assist is another feature of PDA and is designed to detect the lines of the roadway and vary the assistance from the power steering to help the driver stay within the lanePROACTIVE DRIVING ASSIST WHAT’S NEW。
脉振高频电压注入 PMSM 凸极特性实验检测研究
脉振高频电压注入 PMSM 凸极特性实验检测研究王志新;林环城;陆斌锋;张超【摘要】The salient characteristic of permanent magnet synchronous motor and the impact of cross-satu-ration effect under different operating states are usually attained by time-consuming finite element analysis or complex experiment. A simple experimental method was proposed to detect salient characteristic of permanent magnet synchronous motor. A pulsating high-frequency voltage signal was injected into a rotor-locked motor, and the salient characteristic was obtained by demodulating the high-frequency current re-sponse with rotating coordinate transformation. An experiment on an interior permanent magnet synchro-nous motor was carried out to verify the proposed method, and the experimental results show that this method can detect the salient characteristic of the motor under different working states accurately and ana-lyze the cross-saturation angle caused by cross-saturation effect.%针对传统方法在研究永磁同步电机不同工作点下的凸极特性及分析其受交叉饱和效应影响的程度时通常采用耗时巨大的有限元仿真分析或复杂繁琐的实验方法的问题，提出了一种较为简便的永磁同步电机凸极特性实验检测方法。
无位置传感器无刷直流电机闭环起动方法
无位置传感器无刷直流电机闭环起动方法王强;王友仁;王岭;徐旭明【摘要】针对无位置传感器无刷直流电机起动性能易受负载影响的问题,提出了一种基于直接反电动势的闭环起动新方法.起动包括预定位、加速和切换运行3个过程.分析了加速过程中依据非导通相端电压进行换相的原理,提出了换相点端电压阈值的确定方法.加速过程中,在PWM周期中的ON时刻采样非导通相端电压,并与设定的端电压阈值进行比较来确定换相点;切换运行状态下,通过检测非导通相反电动势过零点延时30°电角度来确定换相点.该方法实现了电机起动过程中位置的闭环控制,因此在不同的负载下均能顺利起动,并且检测电路简单、成本低,便于实现.实验结果表明了所提方法的有效性.【期刊名称】《电机与控制学报》【年(卷),期】2013(017)011【总页数】6页(P41-46)【关键词】无刷直流电机;无位置传感器;反电动势;端电压;起动【作者】王强;王友仁;王岭;徐旭明【作者单位】南京航空航天大学自动化学院,江苏南京210016;南京航空航天大学自动化学院,江苏南京210016;南京航空航天大学自动化学院,江苏南京210016;南京航空航天大学自动化学院,江苏南京210016【正文语种】中文【中图分类】TM3510 引言无刷直流电机（brushless DC motor，BLDCM）具有结构简单、维护方便、调速性能好、运行效率高等优点，广泛应用于商业、工业、航空航天等领域。
传统的无刷直流电机为了能正确换相，需要一套位置传感器来获得转子相对定子的位置信息。
但是，位置传感器的存在不仅增加了电机的成本、体积而且限制了电机在高温、高湿、强腐蚀性气体等恶劣环境下的应用。
针对该问题，国内外学者提出了多种无位置传感器控制方法，主要有反电动势法［1］、磁链法［2］、电感法［3］及人工智能法［4－8］等。
其中，反电动势法具有简单、实用等优点，在工程上得到了广泛应用，但是当电机静止时反电动势为零，电机无法实现自起动，针对该问题，国内外学者提出了多种起动方法。
高级驾驶辅助系统ADAS各功能详解
高级驾驶辅助系统（Advanced Driver Assistant System），简称ADAS，是利用安装于车上的各式各样的传感器，在第一时间收集车内外的环境数据，进行静、动态物体的辨识、侦测与追踪等技术上的处理，从而能够让驾驶者在最快的时间察觉可能发生的危险，以引起注意和提高安全性的主动安全技术。
ADAS 采用的传感器主要有摄像头、雷达、激光和超声波等，可以探测光、热、压力或其它用于监测汽车状态的变量，通常位于车辆的前后保险杠、侧视镜、驾驶杆内部或者挡风玻璃上。
早期的ADAS 技术主要以被动式报警为主，当车辆检测到潜在危险时，会发出警报提醒驾车者注意异常的车辆或道路情况。
对于最新的ADAS 技术来说，主动式干预也很常见。
ADAS通常包括以下17种用与汽车驾驶辅助的系统：1、导航：导航是一个研究领域，重点是监测和控制工艺或车辆从一个地方移动到另一个地方的过程。
导航领域包括四个一般类别：陆地导航，海洋导航，航空导航和空间导航。
2、时交通系统TMC：TMC是是欧洲的辅助GPS导航的功能系统。
它是通过RDS方式发送实时交通信息和天气状况的一种开放式数据应用。
借助于具有TMC功能的导航系统，数据信息可以被接收并解码，然后以用户语言或可视化的方式将和当前旅行路线相关的信息展现给驾驶者。
3、电子警察系统ISA：我国道路交通管理系统中的“电子警察”是随着科技的发展而产生的，是一个时代的产物。
它作为现代道路交通安全管理的有效手段，可以迅速地监控、抓拍、处理交通违章事件，迅速地获取违章证据，提供行之有效的监测手段，为改善城市交通拥堵现象起到了重要的作用，已成为道路交通管理队伍中必不可少的一员，以充分发挥它准确、公正的执法作用。
4、车联网（Internet of Vehicles）：车联网是由车辆位置、速度和路线等信息构成的巨大交互网络。
通过GPS、RFID、传感器、摄像头图像处理等装置，车辆可以完成自身环境和状态信息的采集；通过互联网技术，所有的车辆可以将自身的各种信息传输汇聚到中央处理器；通过计算机技术，这些大量车辆的信息可以被分析和处理，从而计算出不同车辆的最佳路线、及时汇报路况和安排信号灯周期5、自适应巡航ACC（Adaptivecruise control）：自适应巡航控制系统是一种智能化的自动控制系统，它是在早已存在的巡航控制技术的基础上发展而来的。
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New advances in the development of fast semiconductor switches and cost-effective DSP processors have revolutionized the adjustable speed motor drives. These new opportunitieshave contributedto the field of motor drives by introducing novel configurations for electric machines in which the burden is shifted from complicated hardware structures onto software and control algorithms. This in turn has resulted in considerable improvement in cost while upgrading the performance of the overall drive system. The BLDC motor drive system is the most illustrative example of this trend. Very compact geometry and an impressive efficiency, along with a very simple control are among the main attractions for replacing many adjustable ih speed applications wt this emerging technology. The BLDC motor is increasingly being used in computer, aerospace, military, automotive, industrial and household products because of its high torque, compactness, and high eficiency. A BLDC motor with higher number of phases has several advantages compare to the conventional three-phase BLDC motors. It can reduce the torque ripple and increase the efficiency and iron utilization without increasing the voltage per phase. For mltr applications, the fault tolerant feature iiay is one of the most important considerations. With higher number of phases, a utilized motor can be still operated when one or more phases are failed. Based on these advantages, the multi-phase BLDC motor is considered as a good candidate for energy saving or military applications. The BLDC motor is inherently electronically controlled and requires rotor position information for proper commutation of currents. H w v r the problems of the cost and oee, reliability of rotor position sensors have motivated research in the area of psition sensorless BLDC motor drives. Solving this problem effectively will open the way for full
Tae-Hyung Kim’, Hyung-Woo Lee’, M C ~ ~ J E E E , Mehrdad Ehsani3,FCUOW,IEEE and
Department of Electrical Engineering, Texas A&M University, USA, e-mail: , ehsani@ Department oflleoretical and Applied Mechanics, Comell University, USA, e-mail : hl333@
Where, v, is the active phase voltage; R is the phase resistance; i is the phase current; B is the rotor position; , wk(@,i,) is the total flux linkage of the active phase; and ‘n’ is the number of phases. The flux linkage in the active phase includes both self and mutual flux linkages. For any multi-phase BLDC motors, the total flux linkage of a phase is:
n. MATHEhUTICAL BMlS OF THE m O W S E DSENຫໍສະໝຸດ ORLESS DRIVE METEOD
Each active phase in an AC motor is described by a first order differential equation. The general voltage equation of one of the active phases is given by,
Absfruct- The main purpose of tbls paper Is to develop a new posjtion sensorless drive method for multi-phase brushleas DC (BLDC) motors. A BLDC motor with higher number of phases has several advantages. It can reduce the Ampereturns and torque puIsalion and Increase the efficiency and h n utilization. Also, it is well used for fault tolerant motor drive systems 85 in military applications. With the developed speed-independent, new p c aiiios information that i computed i a microprocessor, highly s n accurate sensorless operation can be reabed for multLphase BLDC m t r . The operational principle of the proposed senoos sorless drive for multi-phase BLDC motors is theoretically analyzed and the performance is demonstrated by computer aimulatlons and experiments i detail. n
penetration of this motor drive into all low cost, high reliability, and large volume applications. Most popular and practical sensorless drive methods for BLDC motors rely on speed-dependent back-EMF Ill-[4]. Since the back-EMF is zero or undetectably small at standstill and low speeds, it is not possible to use the back-EMF sensing method at low speed range. Also, the estimated commutation points that are shified by 30” fiom zero crossing of back-EMFs have position error in transient state. The flux estimation method [5]-[ti] also has significant estimation error at low speed, in which the voltage equation is integrated in a relatively large period of time. In this paper, we propose a novel sensorless drive technique for multi-phase BLDC motors that covers wide speed range from near zero to high speed without additional h r w r for sensing terminal voltages. This sensorless opadae eration technique is very suitable for multi-phase BLDC motors that are gaining lots of interest for various applications. This sensorless method provides continuous rotor position informationwith good accuracy and resolution even at very IOW speed operation. This feature makes it suitabIe for high performance applications. Furthermore, the proposed sensorless algorithm is simple enough to implement in real-time using an economical, fixed-point microprocessor. The validity of the developed sensorless drive method for multi-phase BLDC motors has been verified by the computer simulationsand experiment.