An Improved Stochastic Gradient Algorithm to Identify PMSM Parameters Based on CAR Models
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- 英文论文题名：An Improved Stochastic Gradient Algorithm to Identify PMSM Parameters Based on CAR Models
- 论文作者：Hui-jie Sun ; Rui-Qi Dong ; Ying Zhang
- 英文论文作者：Hui-jie Sun ; Rui-Qi Dong ; Ying Zhang ; Harbin Institute of Technology Shenzhen Graduate School
- 年：2017
- 作者机构：Harbin Institute of Technology Shenzhen Graduate School;
- 英文论文关键词：Parameter Identification ; Convergence Analysis ; PMSM ; Stochastic Gradient Algorithm
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：TM341
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：6
- 文件大小：315k
- 原文格式：D
- 会议级别：全国
摘要
In this paper, we study the parameters identification problem of Permanent Magnet Synchronous Motor(PMSM) in steady state. First, the controlled auto-regressive(CAR) model of PMSM is established. Secondly, based on the obtained CAR model, an improved stochastic gradient algorithm is proposed to identify the electrical parameters of PMSM. By introducing a tuning parameter in the presented algorithm, the current estimation for the unknown PMSM parameters is updated by using the information not only in the current step but also in the previous step. In addition, a convergence result is provided for the developed algorithm. Finally, an example is given to show the advantage of the proposed algorithm for the parameters identification of PMSM.
In this paper, we study the parameters identification problem of Permanent Magnet Synchronous Motor(PMSM) in steady state. First, the controlled auto-regressive(CAR) model of PMSM is established. Secondly, based on the obtained CAR model, an improved stochastic gradient algorithm is proposed to identify the electrical parameters of PMSM. By introducing a tuning parameter in the presented algorithm, the current estimation for the unknown PMSM parameters is updated by using the information not only in the current step but also in the previous step. In addition, a convergence result is provided for the developed algorithm. Finally, an example is given to show the advantage of the proposed algorithm for the parameters identification of PMSM.
In this paper, we study the parameters identification problem of Permanent Magnet Synchronous Motor(PMSM) in steady state. First, the controlled auto-regressive(CAR) model of PMSM is established. Secondly, based on the obtained CAR model, an improved stochastic gradient algorithm is proposed to identify the electrical parameters of PMSM. By introducing a tuning parameter in the presented algorithm, the current estimation for the unknown PMSM parameters is updated by using the information not only in the current step but also in the previous step. In addition, a convergence result is provided for the developed algorithm. Finally, an example is given to show the advantage of the proposed algorithm for the parameters identification of PMSM.
引文
[1]Yang T,Schinstock D E.Systematic design of current control system for permanent magnet synchronous motors.International Journal of Control,Automation and Systems,2013,11(6):1128-1137.
[2]W.Leonhard,Control of Electrical Drives.New York:Springer-Verlag,1996.
[3]Khov M,Regnier J,Faucher J.On-line parameter estimation of PMSM in open loop and closed loop[C]//2009 IEEE International Conference on Industrial Technology.2009.
[4]Song W,Gang Y,Zhi-Jian Q,et al.Identification of PMSM based on EKF and elman neural network[C]//2009 IEEE International Conference on Automation and Logistics.2009:1459-1463.
[5]Wang L,Xie L,Wang X.The residual based interactive stochastic gradient algorithms for controlled moving average models[J].Applied Mathematics and Computation,2009,211(2):442-449.
[6]Hu Y,Liu B,Zhou Q.A multi-innovation generalized extended stochastic gradient algorithm for output nonlinear autoregressive moving average systems[J].Applied Mathematics and Computation,2014,247:218-224.
[7]Liu Y,Xiao Y,Zhao X.Multi-innovation stochastic gradient algorithm for multiple-input single-output systems using the auxiliary model[J].Applied Mathematics and Computation,2009,215(4):1477-1483.
[8]Li J,Hua C,Tang Y,et al.Stochastic gradient with changing forgetting factor-based parameter identification for Wiener systems[J].Applied Mathematics Letters,2014,33:40-45.
[9]Li J,Hua C,Tang Y,et al.A time-varying forgetting factor stochastic gradient combined with Kalman filter algorithm for parameter identification of dynamic systems[J].Nonlinear Dynamics,2014,78(3):1943-1952.
[10]Xiao Y,Wang D,Ding F.The residual-based ESG algorithm and its performance analysis[J].Journal of the Franklin Institute,2010,347(2):426-437.
[11]Wang D,Ding F.Performance analysis of the auxiliary models based multi-innovation stochastic gradient estimation algorithm for output error systems[J].Digital Signal Processing,2010,20(3):750-762.
[12]Mao Y,Ding F.Multi-innovation stochastic gradient identification for Hammerstein controlled autoregressive autoregressive systems based on the filtering technique[J].Nonlinear Dynamics,2015,79(3):1745-1755.
[13]Ding F,Liu G,Liu X P.Parameter estimation with scarce measurements[J].Automatica,2011,47(8):1646-1655.
[2]W.Leonhard,Control of Electrical Drives.New York:Springer-Verlag,1996.
[3]Khov M,Regnier J,Faucher J.On-line parameter estimation of PMSM in open loop and closed loop[C]//2009 IEEE International Conference on Industrial Technology.2009.
[4]Song W,Gang Y,Zhi-Jian Q,et al.Identification of PMSM based on EKF and elman neural network[C]//2009 IEEE International Conference on Automation and Logistics.2009:1459-1463.
[5]Wang L,Xie L,Wang X.The residual based interactive stochastic gradient algorithms for controlled moving average models[J].Applied Mathematics and Computation,2009,211(2):442-449.
[6]Hu Y,Liu B,Zhou Q.A multi-innovation generalized extended stochastic gradient algorithm for output nonlinear autoregressive moving average systems[J].Applied Mathematics and Computation,2014,247:218-224.
[7]Liu Y,Xiao Y,Zhao X.Multi-innovation stochastic gradient algorithm for multiple-input single-output systems using the auxiliary model[J].Applied Mathematics and Computation,2009,215(4):1477-1483.
[8]Li J,Hua C,Tang Y,et al.Stochastic gradient with changing forgetting factor-based parameter identification for Wiener systems[J].Applied Mathematics Letters,2014,33:40-45.
[9]Li J,Hua C,Tang Y,et al.A time-varying forgetting factor stochastic gradient combined with Kalman filter algorithm for parameter identification of dynamic systems[J].Nonlinear Dynamics,2014,78(3):1943-1952.
[10]Xiao Y,Wang D,Ding F.The residual-based ESG algorithm and its performance analysis[J].Journal of the Franklin Institute,2010,347(2):426-437.
[11]Wang D,Ding F.Performance analysis of the auxiliary models based multi-innovation stochastic gradient estimation algorithm for output error systems[J].Digital Signal Processing,2010,20(3):750-762.
[12]Mao Y,Ding F.Multi-innovation stochastic gradient identification for Hammerstein controlled autoregressive autoregressive systems based on the filtering technique[J].Nonlinear Dynamics,2015,79(3):1745-1755.
[13]Ding F,Liu G,Liu X P.Parameter estimation with scarce measurements[J].Automatica,2011,47(8):1646-1655.