感应电机状态估计和参数辨识若干新方法研究
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摘要
交流电机以其经济和技术优势占据了电力传动领域的主导地位,各种高性能的交流调速技术也得到了广泛的研究和应用。转子磁场定向控制使交流调速系统的性能产生了质的飞跃,感应电机无速度传感器控制更是增加了系统的简易性和鲁棒性。感应电机无速度传感器控制系统需要解决的关键问题是电机转速估计和转子磁链观测。
扩展Kalman滤波(EKF)是一种有效的感应电机状态估计算法,但其存在两大缺陷:(1)对电机参数变化的鲁棒性欠佳;(2)对突变状态的跟踪能力较弱。本文利用强跟踪滤波(STF),提出了基于STF的感应电机状态估计算法,有效提高了对于突变状态的估计性能和参数变化的鲁棒性。此外,传统基于EKF的感应电机状态估计算法将电机转速作为常量处理,导致算法在极低速和零速时的估计精度不佳。本文引入电机的机械和转矩方程,将转速作为变量处理,并增加对负载转矩的估计,从而避免零速附近激励信号不足和摩擦阻力影响,提高状态估计精度。
上述建立在感应电机全阶模型基础上的状态估计方法存在高阶矩阵运算,计算量偏大。为此,导出感应电机的降阶模型,此模型的观测量为状态的一阶延迟,无法直接利用EKF进行状态估计,引入延迟扩展Kalman滤波算法(SEKF)实现电机的状态估计。由于SEKF是在EKF的基础上得到的,因此存在与EKF同样的缺陷,利用STF的思想对其进行改进,提出了强跟踪延迟滤波(STSF)算法,并将其应用于转速估计和磁链观测。仿真和实验研究表明,基于STSF的感应电机状态估计算法具有满意的动、静态估计性能,同时计算量也有显著降低。
前面提出的状态估计方法均假设电机参数保持不变,然而感应电机在运行过程中,参数随着工况和环境的变化表现出时变性。仿真研究表明,电机参数变化对EKF和STF的估计精度均会产生不良影响。为了在实践中获得高性能的状态估计,必须对电机参数进行在线辨识。对于定、转子电阻,提出了基于STF的辨识方法,得到了满意的估计精度;对于励磁电感,由于非线性程度较高,直接利用STF估计会增加算法的复杂度,因此提出了基于无轨迹Kalman滤波(UKF)和基于双重EKF(Dual EKF)估计的辨识方法。仿真和实验研究表明,上述方法均能实现对电机参数的准确辨识,从而避免状态估计受电机参数变化的影响。
前面通过在线辨识解决了状态估计方法中的参数自适应问题,然而辨识本身需要一个过程,即当前周期得到的参数辨识结果到下一周期才被更新,因此本质上算法对系统模型的跟踪存在滞后,从而影响算法的动态性能。为此,提出利用多模型(MM)算法对电机状态和参数进行估计。为了降低传统MM算法的计算量,提高估计精度,提出了单滤波器多模型(SFMM)算法,并引入变结构思想,提出了单滤波器变结构多模型(SFVSMM)算法,将其应用于感应电机的状态估计和参数辨识中。仿真和实验研究表明,SFVSMM算法具有满意的状态和参数估计性能,并且计算量适中,为感应电机的无速度传感器控制提供了一种新的解决方案。
最后,利用交流传动互馈实验台对本文提出的感应电机状态估计和参数辨识方法进行了实验研究。利用联合矢量控制对互馈双电机的同轴转速和转矩进行调节,相应的提出了基于STSF的联合状态估计方法,实现对互馈双电机状态的同时估计,能有效提高同轴转速和转矩的估计精度。针对定、转子电阻和励磁电感辨识方法的实验结果同样具有满意的精度,与实际值相当接近,从而满足感应电机高性能无速度传感器控制的要求。
The AC motors have occupied a dominant position in the electrical power drive field based on the advantages of economy and technology. Various high performance AC speed adjustment technologies are widely researched and applied. The rotor field oriented control has brought essential advances in AC speed adjustment system. Speed sensorless control of induction motor (IM) promotes the simplicity and robustness further. There are two problems must be solved in this system: the speed estimation and rotor flux observation.
Extended Kalman Filter (EKF) is an effective state estimation algorithm of IM. But it has two major defects: (1) bad robustness to the variation of motor parameters; (2) bad tracking ability to the abrupt change of states. To overcome these disadvantages, the Strong Track Filter (STF) is introduced to estimate the motor states, which can improve the estimation performance of abrupt change states and the robustness of variable parameters. Besides, the speed is considered as a constant in the traditional EKF-based estimator, which results to the bad estimation precision at very low and zero speed. In this paper, the mechanism and torque equations are introduced into the model of IM. Additionally, the speed is regarded as a variable, and the load torque is added to the state vector. It can improve the speed estimation precision and avoid the effects of lacking signal or friction at very low and zero speed to estimate the load torque.
The state estimators based on full-order model of IM need high-order matrix operations, which has large computational burden. Therefor, the reduced-order model of IM is derived, while its observation equation is the first-order state delay, so its states can't be estimated by EKF directly. Therefore, the Schmidt Extended Kalman Filter (SEKF) is introduced. Since SEKF inherits the basic algorithm of EKF, it has the same disadvantages as EKF. Using the concept of STF to improve it, the Strong Track Schmidt Filter (STSF) is proposed, and is applied to speed estimation and flux observation of IM. The simulation and experiment results illustrate that the STSF-based state estimator of IM has satisfactory dynamic and static estimation performance, and it also has lower computational complexity.
The parameters of IM are supposed as constant in the above proposed state estimation algorithms, but in fact they are time-varying along with the changes of working condition during the operation. Simulation results illuminate that the estimation precisions of EKF and STF are affected by the changes of parameters. In order to obtain the high performance of state estimation, the parameters must be online identified. Therefore, an STF-based identification method is proposed to estimate the stator and rotor resistance, which has satisfactory estimation precision. Since magnetic inductance is highly nonlinear, the algorithm will be complex using STF. Two novel magnetic inductance identification approaches are proposed, one is based on Unscented Kalman Filter (UKF), and the other is based on Dual Extended Kalman Filter (Dual EKF). Simulation and experiment results show that the proposed algorithms can identify the parameters of IM exactly, and avoid the state estimation impacted by parameter variety.
In the above state estimation methods, the problem of parameter self-adaptive is solved by online identification. However, identification needs a process, the result of this cycle will be used at the next cycle, so the track of system model has delay essentially, and the dynamic performance is affected. Therefore, Multiple Model (MM) algorithm is introduced to estimate the states and parameters of IM. In order to increase estimation precision and decrease computational burden, a Single Filter Multiple Model (SFMM) algorithm is proposed. Combined with the variable structure method, a Single Filter Variable Structure Multiple Model (SFVSMM) algorithm is obtained. Simulation and experiment results illustrate that it has satisfactory estimation performance and proper computational burden.
The proposed state and parameter estimation methods in this paper are experimented using the reciprocal power-fed AC drive test-bed. A combined state estimation method is proposed based on STSF, which can estimate the coaxial speed and load torque of two motors simultaneously and increase the precision efficiently. The parameter identification methods also have high precision, which can satisfy the request of high performance speed sensorless control of IM.
扩展Kalman滤波(EKF)是一种有效的感应电机状态估计算法,但其存在两大缺陷:(1)对电机参数变化的鲁棒性欠佳;(2)对突变状态的跟踪能力较弱。本文利用强跟踪滤波(STF),提出了基于STF的感应电机状态估计算法,有效提高了对于突变状态的估计性能和参数变化的鲁棒性。此外,传统基于EKF的感应电机状态估计算法将电机转速作为常量处理,导致算法在极低速和零速时的估计精度不佳。本文引入电机的机械和转矩方程,将转速作为变量处理,并增加对负载转矩的估计,从而避免零速附近激励信号不足和摩擦阻力影响,提高状态估计精度。
上述建立在感应电机全阶模型基础上的状态估计方法存在高阶矩阵运算,计算量偏大。为此,导出感应电机的降阶模型,此模型的观测量为状态的一阶延迟,无法直接利用EKF进行状态估计,引入延迟扩展Kalman滤波算法(SEKF)实现电机的状态估计。由于SEKF是在EKF的基础上得到的,因此存在与EKF同样的缺陷,利用STF的思想对其进行改进,提出了强跟踪延迟滤波(STSF)算法,并将其应用于转速估计和磁链观测。仿真和实验研究表明,基于STSF的感应电机状态估计算法具有满意的动、静态估计性能,同时计算量也有显著降低。
前面提出的状态估计方法均假设电机参数保持不变,然而感应电机在运行过程中,参数随着工况和环境的变化表现出时变性。仿真研究表明,电机参数变化对EKF和STF的估计精度均会产生不良影响。为了在实践中获得高性能的状态估计,必须对电机参数进行在线辨识。对于定、转子电阻,提出了基于STF的辨识方法,得到了满意的估计精度;对于励磁电感,由于非线性程度较高,直接利用STF估计会增加算法的复杂度,因此提出了基于无轨迹Kalman滤波(UKF)和基于双重EKF(Dual EKF)估计的辨识方法。仿真和实验研究表明,上述方法均能实现对电机参数的准确辨识,从而避免状态估计受电机参数变化的影响。
前面通过在线辨识解决了状态估计方法中的参数自适应问题,然而辨识本身需要一个过程,即当前周期得到的参数辨识结果到下一周期才被更新,因此本质上算法对系统模型的跟踪存在滞后,从而影响算法的动态性能。为此,提出利用多模型(MM)算法对电机状态和参数进行估计。为了降低传统MM算法的计算量,提高估计精度,提出了单滤波器多模型(SFMM)算法,并引入变结构思想,提出了单滤波器变结构多模型(SFVSMM)算法,将其应用于感应电机的状态估计和参数辨识中。仿真和实验研究表明,SFVSMM算法具有满意的状态和参数估计性能,并且计算量适中,为感应电机的无速度传感器控制提供了一种新的解决方案。
最后,利用交流传动互馈实验台对本文提出的感应电机状态估计和参数辨识方法进行了实验研究。利用联合矢量控制对互馈双电机的同轴转速和转矩进行调节,相应的提出了基于STSF的联合状态估计方法,实现对互馈双电机状态的同时估计,能有效提高同轴转速和转矩的估计精度。针对定、转子电阻和励磁电感辨识方法的实验结果同样具有满意的精度,与实际值相当接近,从而满足感应电机高性能无速度传感器控制的要求。
The AC motors have occupied a dominant position in the electrical power drive field based on the advantages of economy and technology. Various high performance AC speed adjustment technologies are widely researched and applied. The rotor field oriented control has brought essential advances in AC speed adjustment system. Speed sensorless control of induction motor (IM) promotes the simplicity and robustness further. There are two problems must be solved in this system: the speed estimation and rotor flux observation.
Extended Kalman Filter (EKF) is an effective state estimation algorithm of IM. But it has two major defects: (1) bad robustness to the variation of motor parameters; (2) bad tracking ability to the abrupt change of states. To overcome these disadvantages, the Strong Track Filter (STF) is introduced to estimate the motor states, which can improve the estimation performance of abrupt change states and the robustness of variable parameters. Besides, the speed is considered as a constant in the traditional EKF-based estimator, which results to the bad estimation precision at very low and zero speed. In this paper, the mechanism and torque equations are introduced into the model of IM. Additionally, the speed is regarded as a variable, and the load torque is added to the state vector. It can improve the speed estimation precision and avoid the effects of lacking signal or friction at very low and zero speed to estimate the load torque.
The state estimators based on full-order model of IM need high-order matrix operations, which has large computational burden. Therefor, the reduced-order model of IM is derived, while its observation equation is the first-order state delay, so its states can't be estimated by EKF directly. Therefore, the Schmidt Extended Kalman Filter (SEKF) is introduced. Since SEKF inherits the basic algorithm of EKF, it has the same disadvantages as EKF. Using the concept of STF to improve it, the Strong Track Schmidt Filter (STSF) is proposed, and is applied to speed estimation and flux observation of IM. The simulation and experiment results illustrate that the STSF-based state estimator of IM has satisfactory dynamic and static estimation performance, and it also has lower computational complexity.
The parameters of IM are supposed as constant in the above proposed state estimation algorithms, but in fact they are time-varying along with the changes of working condition during the operation. Simulation results illuminate that the estimation precisions of EKF and STF are affected by the changes of parameters. In order to obtain the high performance of state estimation, the parameters must be online identified. Therefore, an STF-based identification method is proposed to estimate the stator and rotor resistance, which has satisfactory estimation precision. Since magnetic inductance is highly nonlinear, the algorithm will be complex using STF. Two novel magnetic inductance identification approaches are proposed, one is based on Unscented Kalman Filter (UKF), and the other is based on Dual Extended Kalman Filter (Dual EKF). Simulation and experiment results show that the proposed algorithms can identify the parameters of IM exactly, and avoid the state estimation impacted by parameter variety.
In the above state estimation methods, the problem of parameter self-adaptive is solved by online identification. However, identification needs a process, the result of this cycle will be used at the next cycle, so the track of system model has delay essentially, and the dynamic performance is affected. Therefore, Multiple Model (MM) algorithm is introduced to estimate the states and parameters of IM. In order to increase estimation precision and decrease computational burden, a Single Filter Multiple Model (SFMM) algorithm is proposed. Combined with the variable structure method, a Single Filter Variable Structure Multiple Model (SFVSMM) algorithm is obtained. Simulation and experiment results illustrate that it has satisfactory estimation performance and proper computational burden.
The proposed state and parameter estimation methods in this paper are experimented using the reciprocal power-fed AC drive test-bed. A combined state estimation method is proposed based on STSF, which can estimate the coaxial speed and load torque of two motors simultaneously and increase the precision efficiently. The parameter identification methods also have high precision, which can satisfy the request of high performance speed sensorless control of IM.
引文
[1]B.K.Bose.Modern Power Electronics and AC Drives.北京:机械工业出版社,2003.
[2]沈本荫.现代交流传动及其控制系统.北京:中国铁道出版社,1997.
[3]T.M.Jahns,V.Blasko.Recent advances in power electronics technology for industrial and traction machine drives.Proceedings of the IEEE,2001,89(6):963-975.
[4]F.Blaschke.A new method for the structure decoupling of AC induction machines.Conference Record of the 2~(nd) IFAC Symposium on Multivariable Technical Control Systems,1971:11-15.
[5]M.Depenbrock.Direct self-control(DSC) of inverter-fed induction machine.IEEE Trans.on Power Electronics,1988,3(4):420-429.
[6]K.Rajashekara,A.Kawamura,K.Matsuse.Sensorless Control of AC Motor Drives.New York:IEEE Press,1996.
[7]杨耕,陈伯时.交流感应电动机无速度传感器的高动态性能控制方法综述.电气传动,2001,(3):3-8.
[8]J.Holtz.Sensorless control of induction motor drives.Proceedings of the IEEE,2002,90(8):1359-1394.
[9]J.Holtz.Sensorless control of induction machines-with or without signal injection.IEEE Trans.on Industrial Electronics,2006,53(1):7-30.
[10]H.Tajima,Y.Matsumoto.Speed sensorless vector control method for an industrial drive system.IEEE Proceedings of International Power Electronics Conference,1995,1034-1039.
[11]U.Baader.Direct self control of inverter induction machine,a basis for speed control without speed measurement.IEEE Trans.on Industry Application.1992,128(3):581-588.
[12]T.Kanmachi,I.Takahashi.Sensor-less speed control of an induction motor with no influence of secondary resistance variation.IEEE Conference Record of Industry Applications Society Annual Meeting,1993,(1):408-413.
[13]C.Schauder.Adaptive speed identification for vector control of induction motor without rotational transducers.IEEE Trans.on Industry Applications,1992,28(5):1054-1061.
[14]F.Z.Peng,T.Fukao.Robust speed identification for speed sensorless vector control of induction motors.IEEE Trans.on Industry Applications,1994,30(5):1234-1240.
[15]M.Rashed,A.F.Stronach.A stable back-EMF MRAS-based sensorless low-speed induction motor drive insensitive to stator resistance variation.IEE Proceedings of Electric Power Applications,2004,151(6):685-693.
[16]H.M.Kojabadi,L.C.Chang,R.Doraiswami.A MRAS-based adaptive pseudoreduced order flux observer for sensorless induction motor drives.IEEE Trans.on Power Electronics,2005,20(4):930-938.
[17]M.Cirrincione,M.Pucci.An MRAS-based sensorless high-performance induction motor drive with a predictive adaptive model.IEEE Trans.on Industrial Electronics,2005,52(2):532-551.
[18]巫庆辉,邵诚.一种基于锁相环原理的参考模型自适应感应电机转速估计方法.自动化学报,2006,32(5):713-721.
[19]王庆龙,张崇巍,张兴.交流电机无速度传感器矢量控制系统变结构模型参考自适应转速辨识.中国电机工程学报,2007,27(15):70-74.
[20]R.Pena,R.Cardenas,J.Proboste,G.Asher,J.Clare.Sensorless control of doubly-fed induction generators using a rotor-current-based MRAS observer.IEEE Trans.on Industrial Electronics,2008,55(1):330-339.
[21]M.Hinkkanen.Analysis and design of full-order flux observers for sensorless induction motors.IEEE Trans.on Industrial Electronics,2004,51(5):1033-1040.
[22]L.Harnefors.Globally stable speed-adaptive observers for sensorless induction motor drives.IEEE Trans.on Industrial Electronics,2007,54(2):1243-1245.
[23]S.Suwankawin,S.Sangwongwanich.Design strategy of an adaptive full-order observer for speed-sensorless induction-motor drives-tracking performance and stabilization.IEEE Trans.on Industrial Electronic s,2006,53(1):96-119.
[24]黄志武,桂卫华,年晓红,单勇腾,刘心昊.基于自适应观测器的无速度传感器感应电机控制.控制理论与应用,2007,24(6):914-918.
[25]王坚,年晓红,桂卫华,曹宵.新型异步电机无速度传感器控制方法.中国电机工程学报,2008,28(3):96-101.
[26]L.Harnefors,M.Hinkkanen.Complete stability of reduced-order and full-order observers for sensorless IM drives.IEEE Trans.on Industrial Electronics,2007,55(3):1319-1329.
[27]M.Montanari,S.M.Peresada,C.Rossi,A.Tilli.Speed sensorless control of induction motors based on a reduced-order adaptive observer.IEEE Trans.on Control Systems Technology,2007,15(6):1049-1064.
[28]M.Cirrincione,M.Pucci,G.Cirrincione,G.A.Capolino.Sensorless control of induction motors by reduced order observer with MCA EXIN based adaptive speed estimation.IEEE Trans.on Industrial Electronics,2007,54(1):150-166.
[29]A.Derdiyok.Speed-sensorless control of induction motor using a continuous control approach of sliding-mode and flux observer.IEEE Trans.on Industrial Electronics,2005,52(4):1170-1176.
[30]路强,沈传文,季晓隆,孟永庆.一种用于感应电机控制的新型滑模速度观测器研究.中国电机工程学报,2006,26(18):164-168.
[31]N.Inanc.A robust sliding mode flux and speed observer for speed sensorless control of an indirect field oriented induction motor drives.Electric Power Systems Research,2007,77:1681-1688.
[32]R.E.Kalman.A New Approach to Linear Filtering and Prediction Problems.Transaction of the ASME-Journal of Basic Engineering,1960,82(D):35-45.
[33]R.G.Brown,P.Y.C.Hwang.Introduction to random signals and applied Kalman filtering.New York:Wiley Press,1992.
[34]Young-Real Kim,Seung-Ki Sul,Min-Ho Park.Speed sensorless vector control of induction motor using extended Kalman filter.IEEE Trans.on Industry Applications,1994,30(5):1225-1233.
[35]K.L.Shi,T.F.Chan,Y.K.Wong,S.L.Ho.Speed estimation of an induction motor drive using an optimized extended Kalman filter.IEEE Trans.on Industrial Electronics,2002,49(1):124-133.
[36]陆可,肖建.多采样率EKF及其在感应电机转速估计中的应用.西南交通大学学报,2007,42(5):620-625.
[37]M.Menaa,O.Touhami,R.Ibtiouen,M.Fadel.Sensorless direct vector control of an induction motor.Control Engineering Practice,2008,16(1):67-77.
[38]G.Garcia Soto,E.Mendes,A.Razek.Reduced-order observers for rotor flux,rotor resistance and speed estimation for vector controlled induction motor drives using the extended Kalman filter technique.IEE Proceedings of Electric Power Applications,1999,146(3):282-288.
[39]杨文强,李树广,贾正春.基于降阶推广卡尔曼滤波算法的交流感应电动机无速度传感器矢量控制系统.上海交通大学学报,2003,37(9):1362-1365.
[40]A.V.Leite,R.E.Araujo,D.Freitas.Full and reduced order extended Kalman filter for speed estimation in induction motor drives:a comparative study.Proceedings of the 35~(th) Annual IEEE Power Electronics Specialists Conference,2004,3:2293-2299.
[41]M.Barut,S.Bogosyan,M.Gokasan.Speed sensorless estimation for induction motors using extended Kalman filters.IEEE Trans.on Industrial Electronics,2007,54(1):272-280.
[42]S.Bogosyan,M.Barut,M.Gokasan.Braided extended Kalman filters for sensorless estimation in induction motors at high-low/zero speed.IET Control Theory and Applications,2007,1(4):987-998.
[43]S.J.Julier,J.K.Uhlmann.Unscented filtering and nonlinear estimation.Proceedings of the IEEE,2004,92(3):401-422.
[44]潘泉,杨峰,叶亮.一类非线性滤波器-UKF综述.控制与决策.2005,20(5):481-489.
[45]X.L.Ning,J.C.Fang.An autonomous celestial navigation method for LEO satellite based on unscented Kalman filter and information fusion.Aerospace Science and Technology,2007,11(2-3):222-228.
[46]陈记争,袁建平,方群.基于修正Rodrigues参数和UKF的姿态估计算法.宇航学报,2008,29(5):1622-1626.
[47]袁罡,陈鲸.基于UKF的单站无源定位与跟踪算法.电子与信息学报,2008,30(9):2120-2123.
[48]M.N.Petsios,E.G.Alivizatos,N.K.Uzunoglu.Solving the association problem for a multistatic range-only radar target tracker.Signal Processing,2008,88(9):2254-2277.
[49]S.Sarkka.On unscented Kalman filtering for state estimation of continuous-time nonlinear systems.IEEE Trans.on Automatic Control,2007,52(9):1631-1641.
[50]R.Kandepu,B.Foss,L.Imsland.Applying the unscented Kalman filter for nonlinear state estimation.Journal of Process Control,2008,18(7-8):753-768.
[51]Q.Song,J.D.Han.An adaptive UKF algorithm for the state and parameter estimation of a mobile robot.Acta Automatica Sinica,2008,34(1):72-79.
[52]张鹏,黄金泉.基于双重卡尔曼滤波器的发动机故障诊断.航空动力学报,2008,23(5):952-956.
[53]李洁,钟彦儒.无轨迹卡尔曼滤波器在感应电机转速估计中的应用.电工技术学报.2006,21(2):45-50.
[54]陆可,肖建.双UKF算法及其在感应电机矢量控制中的应用.电机与控制学报. 2007,11(6):564-567,572.
[55]周东华,席裕庚,张钟俊.一种带多重次优渐消因子的扩展卡尔曼滤波器.自动化学报,1991,17(6):689-695.
[56]D.H.Zhou,P.M.Frank.Fault diagnostics and fault tolerant control.IEEE Trans.on Aerospace and Electronic Systems,1998,34(2):420-427.
[57]周东华,叶银忠.现代故障诊断与容错控制.北京:清华大学出版社,2000.
[58]吕锋,文成林,安德玺,周东华.基于强跟踪滤波器的变压器绕组故障实时检测研究.电工技术学报,2003,18(3):91-97.
[59]李雄杰,周东华.基于强跟踪滤波器的模拟电路故障在线诊断方法.电工技术学报,2007,22(5):13-17.
[60]陆可,肖建.强跟踪延迟滤波算法及其在感应电机无速度传感器控制中的应用.自动化学报,2008,34(9):1076-1082.
[61]陆可,肖建,陈爽,宫金林.基于强跟踪延迟滤波算法的互馈双电机联合状态估计.中国电机工程学报,2008,28(36):80-86.
[62]M.G.Simoes,B.K.Bose.Neural network based estimation of feedback signals for a vector controlled induction motor drive.IEEE Trans.on Industry Applications,1995,31(3):620-629.
[63]L.Ben-Brahim,S.Tadakuma,A.Akdag.Speed control of induction motor without rotational transducers.IEEE Trans.on Industry Applications,1999,35(4):844-849.
[64]S.H.Kim,T.S.Park,J.Y.Yoo,G.T.Park.Speed-sensorless vector control of an induction motor using neural network speed estimation.IEEE Trans.on Industrial Electronics,2001,48(3):609-614.
[65]R.M.Bharadwai,A.G.Parlos,H.A.Toliyat.Neural speed filtering for sensorless induction motor drives.Control Engineering Practice,2004,12(6):687-706.
[66]M.Cirrincione,M.Pucci,G.Cirrincione,G.A.Capolino.Sensorless control of induction machines by a new neural algorithm:the TLS EXIN neuron.IEEE Trans.on Industrial Electronics,2007,54(1):127-149.
[67]K.D.Hurst,T.G.Habetler,G.Griva.Speed sensorless field-oriented control of induction machines using current harmonic spectral estimation.Conference Record of IEEE Industry Applications Society Annul Meeting,1994,1:601-607.
[68]D.Zinger,F.Profumo,T.A.Lipo,D.W.Novotny.A direct field-oriented controller for induction motor drives using tapped stator windings.IEEE Trans.on Power Electronics,1990,5(4):446-453.
[69]J.Jiang,J.Holtz.High dynamic speed sensorless AC drive with on-line parameter tuning and steady-state accuracy.IEEE Trans.on Industrial Electronics,1997,44(2):240-246.
[70]J.M.Aller,T.G.Habetler,R.G.Harley,R.M.Tallam,S.B.Lee.Sensorless speed measurement of AC machines using analytic wavelet transform.IEEE Trans.on Industry Applications,2002,38(5):1344-1350.
[71]冬雷,李永东.无速度传感器异步电动机极低转速下的矢量控制.清华大学学报(自然科学版),2003,43(9):1169-1172.
[72]J.Holtz,J.Quan.Drift and parameter compensated flux estimator for persistent zero stator frequency operation of sensorless controlled induction motors.IEEE Trans.on Industry Applications,2003,39(4):1052-1060.
[73]A.Consoli,G.Scarcella,A.Testa.Speed-and current-sensorless field-oriented induction motor drive operating at low stator frequencies.IEEE Trans.on Industry Applications,2004,40(1):186-193.
[74]C.Caruana,G.M.Asher,M.Sumner.Performance of HF signal injection techniques for zero-low-frequency vector control of induction machines under sensorless conditions.IEEE Trans.on Industrial Electronics,2006,53(1):225-238.
[75]G.Bottiglieri,A.Consoli,T.A.Lipo.Modeling of saturated induction machines with injected high-frequency signals.IEEE Trans.on Energy Conversion,2007,22(4):819-828.
[76]H.A.Toliyat,E.Levi,M.Raina.A review of RFO induction motor parameter estimation techniques.IEEE Trans.on Energy Conversion,2003,18(2):271-283.
[77]E.B.S.Filho,A.M.N.Lima,C.B.Jacobina.Parameter estimation for induction machines via non-linear least squares method.Proceedings of International Conference on Industrial Electronics,Control and Instrumentation,1991,1:639-643.
[78]M.Cirrincione,M.Pucci,G.Cirrincione,G.A.Capolino.A new experimental application of least-squares techniques for the estimation of the induction motor parameters.IEEE Trans.on Industry Applications,2003,39(5):1247-1256.
[79]K.Y.Wang,J.Chiasson,M.Bodson,L.M.Tolbert.A nonlinear least-squares approach for identification of the induction motor parameter.IEEE Trans.on Automatic Control,2005,50(10):1622-1628.
[80]X.Yu,M.W.Dunnigan,B.W.Williams.A novel rotor resistance identification method for an indirect rotor flux-orientated controlled induction machine system.IEEE Trans. on Power Electronics,2002,17(3):353-364.
[81]V.Vasic,S.N.Vukosavic,E.Levi.A stator resistance estimation scheme for speed sensorless rotor flux oriented induction motor drives.IEEE Trans.on Energy Conversion,2003,18(4):476-483.
[82]H.M.Kojabadi,L.Chang,R.Doraiswami.A novel adaptive observer for very fast estimation of stator resistance in sensorless induction motor drives.Proceedings of IEEE 34~(th) Annual Power Electronics Specialist Conference,2003,3:1455-1459.
[83]金海,黄进.基于模型参考方法的感应电机磁链得自适应观测及参数辨识.电工技术学报,2006,21(1):65-69.
[84]D.P.Marcetic,S.N.Vukosavic.Speed-sensorless AC drives with the rotor time constant parameter updata.IEEE Trans.on Industrial Electronics,2007,54(5):2618-2625.
[85]T.Iwasaki,T.Kataoka.Application of an extended Kalman filter to parameter identification of an induction motor.Conference Record of IEEE Industry Applications Society Annual Meeting,1989,1:248-253.
[86]M.Menaa,O.Touhami,R.Ibtiouen.Estimation of rotor resistance of an induction motor using extended Kalman filter and spiral vector theory.Proceedings of IEEE Conference on Control Applications,2003,2:1262-1266.
[87]K.Radhakrishnan,A.Unnikrishnan,K.G.Balakrishnan.EM based extended Kalman filter for estimation of rotor time-constant of induction motor.Proceedings of IEEE International Symposium on Industrial Electronics,2006,3:2434-2438.
[88]M.Barut,S.Bogosyan,M.Gokasan.Switching EKF technique for rotor and stator resistance estimation in speed sensorless control of IMs.Energy Conversion and Management,2007,48(12):3120-3134.
[89]M.Barut,S.Bogosyan,M.Gokasan.Experimental evaluation of braided EKF for sensorless control of induction motors.IEEE Trans.on Industrial Electronics,2008,55(2):620-632.
[90]汪镭,周围兴,吴启迪.基于Hopfield神经网络的线性系统参数辨识方案及在鼠笼式电机传动系统参数辨识中的应用研究.中国电机工程学报,2001,21(1):9-11.
[91]K.Baburaj,F.R.Muhammed.Stator and rotor resistance observers for induction motor drive using fuzzy logic and artificial neural networks.IEEE Trans.on Energy Conversion,2005,20(4):771-780.
[92]B.Karanayil,M.F.Rahman,C.Grantham.Online stator and rotor resistance estimation scheme using artificial neural networks for vector controlled speed sensorless induction motor drive.IEEE Trans.on Industrial Electronics,2007,54(1):167-176.
[93]M.Wlas,Z.Krzeminski,H.A.Toliyat.Neural-network-based parameter estimations of induction motors.IEEE Trans.on Industrial Electronics,2008,55(4):1783-1794.
[94]黄开胜,童怀,郑泰胜,Q.H.Wu,D.R.Turner.遗传算法在异步电动机动态模型参数识别中的应用.中国电机工程学报,2000,20(8):37-41.
[95]B.Abdelhadi,A.Benoudjit,N.Nait-Said.Application of genetic algorithm with a novel adaptive scheme for the identification of induction machine parameters.IEEE Trans.on Energy Conversion,2005,20(2):284-291.
[96]D.T.Magill.Optimal adaptive estimation of sampled stochastic processes.IEEE Trans.on Automatic Control,1965,10(4):434-439.
[97]G.A.Acherson,K.S.Fu.On state estimation in switching environments.IEEE Trans.on Automatic Control,1970,15(1):10-17.
[98]H.A.P.Blom,Y.Bar-Shalom.The interacting multiple model algorithm for systems with markovian switching coefficients.IEEE Trans.on Automatic Control,1988,33(8):780-783.
[99]E.Mazor,A.Averbuch,Y.Bar-Shalom,J.Dayan.Interacting multiple model methods in target tracking:a survey.IEEE Trans.on Aerospace and Electronic Systems,1998,34(1):103-123.
[100]X.R.Li,V.P.Jilkov.Overview of multiple model methods for maneuvering target tracking.Proceedings of the International Society for Optical Engineering,2003,5204:200-210.
[101]X.R.Li,Z.L.Zhao,X.B.Li.General model-set design methods for multiple-model approach.IEEE Trans.on Automatic Control,2005,50(9):1260-1276.
[102]刘建书,李人厚,张贞耀,刘云龙.交互式多模型算法的模型集设计.控制与决策,2007,22(3):326-328,332.
[103]X.R.Li,V.P.Jilkov.Survey of maneuvering target tracking-Part Ⅴ:Multiple-model methods.IEEE Trans.on Aerospace and Electronic Systems,2005,41(4):1255-1321.
[104]刘贵喜,高恩克,范春宇.改进的交互式多模型粒子滤波跟踪算法.电子与信息学报,2007,29(12):2810-2813.
[105]A.Munir,D.P.Atherton.Target tracking using an adaptive interacting multiple model algorithm.Proceedings of American Control Conference,1994,2:1324-1328.
[106]梁彦,贾宇岗,潘泉.具有参数自适应的交互式多模型算法.控制理论与应用, 2001,18(5):653-656.
[107]X.R.Li,Y.Bar-Shalom.Multiple-model estimation with variable structure.IEEE Trans.on Automatic Control,1996,41(4):478-493.
[108]X.R.Li.Multiple-model estimation with variable structure-Part Ⅱ:model-set adaptation.IEEE Trans.on Automatic Control,2000,45(11):2047-2060.
[109]X.R.Li,X.R.Zhi,Y.M.Zhang.Multiple-model estimation with variable structure-Part Ⅲ:model-group switching algorithm.IEEE Trans.on Aerospace and Electronic Systems,1999,35(1):225-241.
[110]梁彦,潘泉,贾宇岗.基于模型空间分解的交互式多模型算法.西北工业大学学报,2001,19(3):394-397.
[111]戴晓强,刘维亭.基于模糊交互多模型的机动目标跟踪方法.弹箭与制导学报,2007,27(1):34-37.
[112]陈利斌,佟明安.引入神经网络的交互式多模型算法.航空学报,2001,22(1):54-56.
[113]臧荣春,崔平原,崔祜涛,金艺.基于IMM-UKF的组合导航算法.控制理论与应用,2007,24(4):634-638.
[114]W.Farrell.Interaction multiple model filter for tactical ballistic missile tracking.IEEE Trans.on Aerospace and Electronic Systems,2008,44(2):418-426.
[115]孙福明,吴秀清,王鹏伟.转弯机动目标的两层交互多模型跟踪算法.控制理论与应用,2008,25(2):233-236,241.
[116]杨争斌,郭福成,周一宇.迭代IMM机动目标被动单站跟踪算法.宇航学报,2008,29(1):304-310.
[117]S.K.Kim,J.Y.Choi,Y.D.Kim.Fault detection and diagnosis of aircraft actuators using fuzzy-tuning IMM filter.IEEE Trans.on Aerospace and Electronic Systems,2008,44(3):940-952.
[118]I.Rapoport,Y.Oshman.Efficient fault tolerant estimation using the IMM methodology.IEEE Trans.on Aerospace and Electronic Systems,2007,43(2):492-508.
[119]陆可,肖建.IMM算法实现非线性状态估计的研究与仿真.计算机仿真,2008,25(5):77-80.104.
[120]陆可,肖建.基于单滤波器多模型算法的感应电机参数自适应转速估计.电工技术学报,2009,24(1):70-75.
[121]L.Chin.Advance in computational efficiencies of linear filtering.Control and Dynamic Systems,New York:Academic Press,1983,19:125-192.
[122]霍连文,郭建斌.采用双变流器-电机能量互馈的交流传动试验系统.机车电传动,2004,4:49-52.
[123]郑琼林,林飞.现代轨道交通与交流传动互馈试验台.电工技术学报,2005,20(1):21-25.
[124]Z.W.Ma,T.Zheng,F.Lin.Research on reciprocal power-fed AC drive test rig for electric traction applications.Proceeding of the 8~(th) International Conference on Electrical Machines and Systems,2005,3:1873-1876.
[125]马志文.电力牵引交流传动互馈试验系统的研究.北京交通大学博士学位论文,2007.
[2]沈本荫.现代交流传动及其控制系统.北京:中国铁道出版社,1997.
[3]T.M.Jahns,V.Blasko.Recent advances in power electronics technology for industrial and traction machine drives.Proceedings of the IEEE,2001,89(6):963-975.
[4]F.Blaschke.A new method for the structure decoupling of AC induction machines.Conference Record of the 2~(nd) IFAC Symposium on Multivariable Technical Control Systems,1971:11-15.
[5]M.Depenbrock.Direct self-control(DSC) of inverter-fed induction machine.IEEE Trans.on Power Electronics,1988,3(4):420-429.
[6]K.Rajashekara,A.Kawamura,K.Matsuse.Sensorless Control of AC Motor Drives.New York:IEEE Press,1996.
[7]杨耕,陈伯时.交流感应电动机无速度传感器的高动态性能控制方法综述.电气传动,2001,(3):3-8.
[8]J.Holtz.Sensorless control of induction motor drives.Proceedings of the IEEE,2002,90(8):1359-1394.
[9]J.Holtz.Sensorless control of induction machines-with or without signal injection.IEEE Trans.on Industrial Electronics,2006,53(1):7-30.
[10]H.Tajima,Y.Matsumoto.Speed sensorless vector control method for an industrial drive system.IEEE Proceedings of International Power Electronics Conference,1995,1034-1039.
[11]U.Baader.Direct self control of inverter induction machine,a basis for speed control without speed measurement.IEEE Trans.on Industry Application.1992,128(3):581-588.
[12]T.Kanmachi,I.Takahashi.Sensor-less speed control of an induction motor with no influence of secondary resistance variation.IEEE Conference Record of Industry Applications Society Annual Meeting,1993,(1):408-413.
[13]C.Schauder.Adaptive speed identification for vector control of induction motor without rotational transducers.IEEE Trans.on Industry Applications,1992,28(5):1054-1061.
[14]F.Z.Peng,T.Fukao.Robust speed identification for speed sensorless vector control of induction motors.IEEE Trans.on Industry Applications,1994,30(5):1234-1240.
[15]M.Rashed,A.F.Stronach.A stable back-EMF MRAS-based sensorless low-speed induction motor drive insensitive to stator resistance variation.IEE Proceedings of Electric Power Applications,2004,151(6):685-693.
[16]H.M.Kojabadi,L.C.Chang,R.Doraiswami.A MRAS-based adaptive pseudoreduced order flux observer for sensorless induction motor drives.IEEE Trans.on Power Electronics,2005,20(4):930-938.
[17]M.Cirrincione,M.Pucci.An MRAS-based sensorless high-performance induction motor drive with a predictive adaptive model.IEEE Trans.on Industrial Electronics,2005,52(2):532-551.
[18]巫庆辉,邵诚.一种基于锁相环原理的参考模型自适应感应电机转速估计方法.自动化学报,2006,32(5):713-721.
[19]王庆龙,张崇巍,张兴.交流电机无速度传感器矢量控制系统变结构模型参考自适应转速辨识.中国电机工程学报,2007,27(15):70-74.
[20]R.Pena,R.Cardenas,J.Proboste,G.Asher,J.Clare.Sensorless control of doubly-fed induction generators using a rotor-current-based MRAS observer.IEEE Trans.on Industrial Electronics,2008,55(1):330-339.
[21]M.Hinkkanen.Analysis and design of full-order flux observers for sensorless induction motors.IEEE Trans.on Industrial Electronics,2004,51(5):1033-1040.
[22]L.Harnefors.Globally stable speed-adaptive observers for sensorless induction motor drives.IEEE Trans.on Industrial Electronics,2007,54(2):1243-1245.
[23]S.Suwankawin,S.Sangwongwanich.Design strategy of an adaptive full-order observer for speed-sensorless induction-motor drives-tracking performance and stabilization.IEEE Trans.on Industrial Electronic s,2006,53(1):96-119.
[24]黄志武,桂卫华,年晓红,单勇腾,刘心昊.基于自适应观测器的无速度传感器感应电机控制.控制理论与应用,2007,24(6):914-918.
[25]王坚,年晓红,桂卫华,曹宵.新型异步电机无速度传感器控制方法.中国电机工程学报,2008,28(3):96-101.
[26]L.Harnefors,M.Hinkkanen.Complete stability of reduced-order and full-order observers for sensorless IM drives.IEEE Trans.on Industrial Electronics,2007,55(3):1319-1329.
[27]M.Montanari,S.M.Peresada,C.Rossi,A.Tilli.Speed sensorless control of induction motors based on a reduced-order adaptive observer.IEEE Trans.on Control Systems Technology,2007,15(6):1049-1064.
[28]M.Cirrincione,M.Pucci,G.Cirrincione,G.A.Capolino.Sensorless control of induction motors by reduced order observer with MCA EXIN based adaptive speed estimation.IEEE Trans.on Industrial Electronics,2007,54(1):150-166.
[29]A.Derdiyok.Speed-sensorless control of induction motor using a continuous control approach of sliding-mode and flux observer.IEEE Trans.on Industrial Electronics,2005,52(4):1170-1176.
[30]路强,沈传文,季晓隆,孟永庆.一种用于感应电机控制的新型滑模速度观测器研究.中国电机工程学报,2006,26(18):164-168.
[31]N.Inanc.A robust sliding mode flux and speed observer for speed sensorless control of an indirect field oriented induction motor drives.Electric Power Systems Research,2007,77:1681-1688.
[32]R.E.Kalman.A New Approach to Linear Filtering and Prediction Problems.Transaction of the ASME-Journal of Basic Engineering,1960,82(D):35-45.
[33]R.G.Brown,P.Y.C.Hwang.Introduction to random signals and applied Kalman filtering.New York:Wiley Press,1992.
[34]Young-Real Kim,Seung-Ki Sul,Min-Ho Park.Speed sensorless vector control of induction motor using extended Kalman filter.IEEE Trans.on Industry Applications,1994,30(5):1225-1233.
[35]K.L.Shi,T.F.Chan,Y.K.Wong,S.L.Ho.Speed estimation of an induction motor drive using an optimized extended Kalman filter.IEEE Trans.on Industrial Electronics,2002,49(1):124-133.
[36]陆可,肖建.多采样率EKF及其在感应电机转速估计中的应用.西南交通大学学报,2007,42(5):620-625.
[37]M.Menaa,O.Touhami,R.Ibtiouen,M.Fadel.Sensorless direct vector control of an induction motor.Control Engineering Practice,2008,16(1):67-77.
[38]G.Garcia Soto,E.Mendes,A.Razek.Reduced-order observers for rotor flux,rotor resistance and speed estimation for vector controlled induction motor drives using the extended Kalman filter technique.IEE Proceedings of Electric Power Applications,1999,146(3):282-288.
[39]杨文强,李树广,贾正春.基于降阶推广卡尔曼滤波算法的交流感应电动机无速度传感器矢量控制系统.上海交通大学学报,2003,37(9):1362-1365.
[40]A.V.Leite,R.E.Araujo,D.Freitas.Full and reduced order extended Kalman filter for speed estimation in induction motor drives:a comparative study.Proceedings of the 35~(th) Annual IEEE Power Electronics Specialists Conference,2004,3:2293-2299.
[41]M.Barut,S.Bogosyan,M.Gokasan.Speed sensorless estimation for induction motors using extended Kalman filters.IEEE Trans.on Industrial Electronics,2007,54(1):272-280.
[42]S.Bogosyan,M.Barut,M.Gokasan.Braided extended Kalman filters for sensorless estimation in induction motors at high-low/zero speed.IET Control Theory and Applications,2007,1(4):987-998.
[43]S.J.Julier,J.K.Uhlmann.Unscented filtering and nonlinear estimation.Proceedings of the IEEE,2004,92(3):401-422.
[44]潘泉,杨峰,叶亮.一类非线性滤波器-UKF综述.控制与决策.2005,20(5):481-489.
[45]X.L.Ning,J.C.Fang.An autonomous celestial navigation method for LEO satellite based on unscented Kalman filter and information fusion.Aerospace Science and Technology,2007,11(2-3):222-228.
[46]陈记争,袁建平,方群.基于修正Rodrigues参数和UKF的姿态估计算法.宇航学报,2008,29(5):1622-1626.
[47]袁罡,陈鲸.基于UKF的单站无源定位与跟踪算法.电子与信息学报,2008,30(9):2120-2123.
[48]M.N.Petsios,E.G.Alivizatos,N.K.Uzunoglu.Solving the association problem for a multistatic range-only radar target tracker.Signal Processing,2008,88(9):2254-2277.
[49]S.Sarkka.On unscented Kalman filtering for state estimation of continuous-time nonlinear systems.IEEE Trans.on Automatic Control,2007,52(9):1631-1641.
[50]R.Kandepu,B.Foss,L.Imsland.Applying the unscented Kalman filter for nonlinear state estimation.Journal of Process Control,2008,18(7-8):753-768.
[51]Q.Song,J.D.Han.An adaptive UKF algorithm for the state and parameter estimation of a mobile robot.Acta Automatica Sinica,2008,34(1):72-79.
[52]张鹏,黄金泉.基于双重卡尔曼滤波器的发动机故障诊断.航空动力学报,2008,23(5):952-956.
[53]李洁,钟彦儒.无轨迹卡尔曼滤波器在感应电机转速估计中的应用.电工技术学报.2006,21(2):45-50.
[54]陆可,肖建.双UKF算法及其在感应电机矢量控制中的应用.电机与控制学报. 2007,11(6):564-567,572.
[55]周东华,席裕庚,张钟俊.一种带多重次优渐消因子的扩展卡尔曼滤波器.自动化学报,1991,17(6):689-695.
[56]D.H.Zhou,P.M.Frank.Fault diagnostics and fault tolerant control.IEEE Trans.on Aerospace and Electronic Systems,1998,34(2):420-427.
[57]周东华,叶银忠.现代故障诊断与容错控制.北京:清华大学出版社,2000.
[58]吕锋,文成林,安德玺,周东华.基于强跟踪滤波器的变压器绕组故障实时检测研究.电工技术学报,2003,18(3):91-97.
[59]李雄杰,周东华.基于强跟踪滤波器的模拟电路故障在线诊断方法.电工技术学报,2007,22(5):13-17.
[60]陆可,肖建.强跟踪延迟滤波算法及其在感应电机无速度传感器控制中的应用.自动化学报,2008,34(9):1076-1082.
[61]陆可,肖建,陈爽,宫金林.基于强跟踪延迟滤波算法的互馈双电机联合状态估计.中国电机工程学报,2008,28(36):80-86.
[62]M.G.Simoes,B.K.Bose.Neural network based estimation of feedback signals for a vector controlled induction motor drive.IEEE Trans.on Industry Applications,1995,31(3):620-629.
[63]L.Ben-Brahim,S.Tadakuma,A.Akdag.Speed control of induction motor without rotational transducers.IEEE Trans.on Industry Applications,1999,35(4):844-849.
[64]S.H.Kim,T.S.Park,J.Y.Yoo,G.T.Park.Speed-sensorless vector control of an induction motor using neural network speed estimation.IEEE Trans.on Industrial Electronics,2001,48(3):609-614.
[65]R.M.Bharadwai,A.G.Parlos,H.A.Toliyat.Neural speed filtering for sensorless induction motor drives.Control Engineering Practice,2004,12(6):687-706.
[66]M.Cirrincione,M.Pucci,G.Cirrincione,G.A.Capolino.Sensorless control of induction machines by a new neural algorithm:the TLS EXIN neuron.IEEE Trans.on Industrial Electronics,2007,54(1):127-149.
[67]K.D.Hurst,T.G.Habetler,G.Griva.Speed sensorless field-oriented control of induction machines using current harmonic spectral estimation.Conference Record of IEEE Industry Applications Society Annul Meeting,1994,1:601-607.
[68]D.Zinger,F.Profumo,T.A.Lipo,D.W.Novotny.A direct field-oriented controller for induction motor drives using tapped stator windings.IEEE Trans.on Power Electronics,1990,5(4):446-453.
[69]J.Jiang,J.Holtz.High dynamic speed sensorless AC drive with on-line parameter tuning and steady-state accuracy.IEEE Trans.on Industrial Electronics,1997,44(2):240-246.
[70]J.M.Aller,T.G.Habetler,R.G.Harley,R.M.Tallam,S.B.Lee.Sensorless speed measurement of AC machines using analytic wavelet transform.IEEE Trans.on Industry Applications,2002,38(5):1344-1350.
[71]冬雷,李永东.无速度传感器异步电动机极低转速下的矢量控制.清华大学学报(自然科学版),2003,43(9):1169-1172.
[72]J.Holtz,J.Quan.Drift and parameter compensated flux estimator for persistent zero stator frequency operation of sensorless controlled induction motors.IEEE Trans.on Industry Applications,2003,39(4):1052-1060.
[73]A.Consoli,G.Scarcella,A.Testa.Speed-and current-sensorless field-oriented induction motor drive operating at low stator frequencies.IEEE Trans.on Industry Applications,2004,40(1):186-193.
[74]C.Caruana,G.M.Asher,M.Sumner.Performance of HF signal injection techniques for zero-low-frequency vector control of induction machines under sensorless conditions.IEEE Trans.on Industrial Electronics,2006,53(1):225-238.
[75]G.Bottiglieri,A.Consoli,T.A.Lipo.Modeling of saturated induction machines with injected high-frequency signals.IEEE Trans.on Energy Conversion,2007,22(4):819-828.
[76]H.A.Toliyat,E.Levi,M.Raina.A review of RFO induction motor parameter estimation techniques.IEEE Trans.on Energy Conversion,2003,18(2):271-283.
[77]E.B.S.Filho,A.M.N.Lima,C.B.Jacobina.Parameter estimation for induction machines via non-linear least squares method.Proceedings of International Conference on Industrial Electronics,Control and Instrumentation,1991,1:639-643.
[78]M.Cirrincione,M.Pucci,G.Cirrincione,G.A.Capolino.A new experimental application of least-squares techniques for the estimation of the induction motor parameters.IEEE Trans.on Industry Applications,2003,39(5):1247-1256.
[79]K.Y.Wang,J.Chiasson,M.Bodson,L.M.Tolbert.A nonlinear least-squares approach for identification of the induction motor parameter.IEEE Trans.on Automatic Control,2005,50(10):1622-1628.
[80]X.Yu,M.W.Dunnigan,B.W.Williams.A novel rotor resistance identification method for an indirect rotor flux-orientated controlled induction machine system.IEEE Trans. on Power Electronics,2002,17(3):353-364.
[81]V.Vasic,S.N.Vukosavic,E.Levi.A stator resistance estimation scheme for speed sensorless rotor flux oriented induction motor drives.IEEE Trans.on Energy Conversion,2003,18(4):476-483.
[82]H.M.Kojabadi,L.Chang,R.Doraiswami.A novel adaptive observer for very fast estimation of stator resistance in sensorless induction motor drives.Proceedings of IEEE 34~(th) Annual Power Electronics Specialist Conference,2003,3:1455-1459.
[83]金海,黄进.基于模型参考方法的感应电机磁链得自适应观测及参数辨识.电工技术学报,2006,21(1):65-69.
[84]D.P.Marcetic,S.N.Vukosavic.Speed-sensorless AC drives with the rotor time constant parameter updata.IEEE Trans.on Industrial Electronics,2007,54(5):2618-2625.
[85]T.Iwasaki,T.Kataoka.Application of an extended Kalman filter to parameter identification of an induction motor.Conference Record of IEEE Industry Applications Society Annual Meeting,1989,1:248-253.
[86]M.Menaa,O.Touhami,R.Ibtiouen.Estimation of rotor resistance of an induction motor using extended Kalman filter and spiral vector theory.Proceedings of IEEE Conference on Control Applications,2003,2:1262-1266.
[87]K.Radhakrishnan,A.Unnikrishnan,K.G.Balakrishnan.EM based extended Kalman filter for estimation of rotor time-constant of induction motor.Proceedings of IEEE International Symposium on Industrial Electronics,2006,3:2434-2438.
[88]M.Barut,S.Bogosyan,M.Gokasan.Switching EKF technique for rotor and stator resistance estimation in speed sensorless control of IMs.Energy Conversion and Management,2007,48(12):3120-3134.
[89]M.Barut,S.Bogosyan,M.Gokasan.Experimental evaluation of braided EKF for sensorless control of induction motors.IEEE Trans.on Industrial Electronics,2008,55(2):620-632.
[90]汪镭,周围兴,吴启迪.基于Hopfield神经网络的线性系统参数辨识方案及在鼠笼式电机传动系统参数辨识中的应用研究.中国电机工程学报,2001,21(1):9-11.
[91]K.Baburaj,F.R.Muhammed.Stator and rotor resistance observers for induction motor drive using fuzzy logic and artificial neural networks.IEEE Trans.on Energy Conversion,2005,20(4):771-780.
[92]B.Karanayil,M.F.Rahman,C.Grantham.Online stator and rotor resistance estimation scheme using artificial neural networks for vector controlled speed sensorless induction motor drive.IEEE Trans.on Industrial Electronics,2007,54(1):167-176.
[93]M.Wlas,Z.Krzeminski,H.A.Toliyat.Neural-network-based parameter estimations of induction motors.IEEE Trans.on Industrial Electronics,2008,55(4):1783-1794.
[94]黄开胜,童怀,郑泰胜,Q.H.Wu,D.R.Turner.遗传算法在异步电动机动态模型参数识别中的应用.中国电机工程学报,2000,20(8):37-41.
[95]B.Abdelhadi,A.Benoudjit,N.Nait-Said.Application of genetic algorithm with a novel adaptive scheme for the identification of induction machine parameters.IEEE Trans.on Energy Conversion,2005,20(2):284-291.
[96]D.T.Magill.Optimal adaptive estimation of sampled stochastic processes.IEEE Trans.on Automatic Control,1965,10(4):434-439.
[97]G.A.Acherson,K.S.Fu.On state estimation in switching environments.IEEE Trans.on Automatic Control,1970,15(1):10-17.
[98]H.A.P.Blom,Y.Bar-Shalom.The interacting multiple model algorithm for systems with markovian switching coefficients.IEEE Trans.on Automatic Control,1988,33(8):780-783.
[99]E.Mazor,A.Averbuch,Y.Bar-Shalom,J.Dayan.Interacting multiple model methods in target tracking:a survey.IEEE Trans.on Aerospace and Electronic Systems,1998,34(1):103-123.
[100]X.R.Li,V.P.Jilkov.Overview of multiple model methods for maneuvering target tracking.Proceedings of the International Society for Optical Engineering,2003,5204:200-210.
[101]X.R.Li,Z.L.Zhao,X.B.Li.General model-set design methods for multiple-model approach.IEEE Trans.on Automatic Control,2005,50(9):1260-1276.
[102]刘建书,李人厚,张贞耀,刘云龙.交互式多模型算法的模型集设计.控制与决策,2007,22(3):326-328,332.
[103]X.R.Li,V.P.Jilkov.Survey of maneuvering target tracking-Part Ⅴ:Multiple-model methods.IEEE Trans.on Aerospace and Electronic Systems,2005,41(4):1255-1321.
[104]刘贵喜,高恩克,范春宇.改进的交互式多模型粒子滤波跟踪算法.电子与信息学报,2007,29(12):2810-2813.
[105]A.Munir,D.P.Atherton.Target tracking using an adaptive interacting multiple model algorithm.Proceedings of American Control Conference,1994,2:1324-1328.
[106]梁彦,贾宇岗,潘泉.具有参数自适应的交互式多模型算法.控制理论与应用, 2001,18(5):653-656.
[107]X.R.Li,Y.Bar-Shalom.Multiple-model estimation with variable structure.IEEE Trans.on Automatic Control,1996,41(4):478-493.
[108]X.R.Li.Multiple-model estimation with variable structure-Part Ⅱ:model-set adaptation.IEEE Trans.on Automatic Control,2000,45(11):2047-2060.
[109]X.R.Li,X.R.Zhi,Y.M.Zhang.Multiple-model estimation with variable structure-Part Ⅲ:model-group switching algorithm.IEEE Trans.on Aerospace and Electronic Systems,1999,35(1):225-241.
[110]梁彦,潘泉,贾宇岗.基于模型空间分解的交互式多模型算法.西北工业大学学报,2001,19(3):394-397.
[111]戴晓强,刘维亭.基于模糊交互多模型的机动目标跟踪方法.弹箭与制导学报,2007,27(1):34-37.
[112]陈利斌,佟明安.引入神经网络的交互式多模型算法.航空学报,2001,22(1):54-56.
[113]臧荣春,崔平原,崔祜涛,金艺.基于IMM-UKF的组合导航算法.控制理论与应用,2007,24(4):634-638.
[114]W.Farrell.Interaction multiple model filter for tactical ballistic missile tracking.IEEE Trans.on Aerospace and Electronic Systems,2008,44(2):418-426.
[115]孙福明,吴秀清,王鹏伟.转弯机动目标的两层交互多模型跟踪算法.控制理论与应用,2008,25(2):233-236,241.
[116]杨争斌,郭福成,周一宇.迭代IMM机动目标被动单站跟踪算法.宇航学报,2008,29(1):304-310.
[117]S.K.Kim,J.Y.Choi,Y.D.Kim.Fault detection and diagnosis of aircraft actuators using fuzzy-tuning IMM filter.IEEE Trans.on Aerospace and Electronic Systems,2008,44(3):940-952.
[118]I.Rapoport,Y.Oshman.Efficient fault tolerant estimation using the IMM methodology.IEEE Trans.on Aerospace and Electronic Systems,2007,43(2):492-508.
[119]陆可,肖建.IMM算法实现非线性状态估计的研究与仿真.计算机仿真,2008,25(5):77-80.104.
[120]陆可,肖建.基于单滤波器多模型算法的感应电机参数自适应转速估计.电工技术学报,2009,24(1):70-75.
[121]L.Chin.Advance in computational efficiencies of linear filtering.Control and Dynamic Systems,New York:Academic Press,1983,19:125-192.
[122]霍连文,郭建斌.采用双变流器-电机能量互馈的交流传动试验系统.机车电传动,2004,4:49-52.
[123]郑琼林,林飞.现代轨道交通与交流传动互馈试验台.电工技术学报,2005,20(1):21-25.
[124]Z.W.Ma,T.Zheng,F.Lin.Research on reciprocal power-fed AC drive test rig for electric traction applications.Proceeding of the 8~(th) International Conference on Electrical Machines and Systems,2005,3:1873-1876.
[125]马志文.电力牵引交流传动互馈试验系统的研究.北京交通大学博士学位论文,2007.