Online identification for permanent magnet synchronous motor based on recursive fixed memory least square method under steady state

Lü, Hao; Wang, Yunkuan; Yuan, Ye; Zheng, Junqiang; Hu, Jianhua; Qin, Xiaofei

doi:10.23919/chicc.2017.8028116

Cited by 12 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to (10), the process of current prediction in the FCS-MPC algorithm is related to key parameters F, G, and back EMF e observed by SMO, which are related to the motor stator resistance and inductance. Therefore, in the sensorless FCS-MPC control algorithm, the stable operation of the system depends on the accurate motor parameters, and the inaccuracy of motor parameters will directly affect the stability and safety of the control system.…”

Section: Sensorless Finite Control Set Model Predictive Control Of Pmsmmentioning

confidence: 99%

“…For example, when observing the parameters of resistance and inductance, the dual-extended Kalman filter is used to identify the motor parameters [9], but it has the drawbacks of high complexity, large computational burden. Although the recursive least-squares method [10,11] can effectively reduce the computational burden, data saturation will occur with the increase of the number of operations. Meanwhile, although multi-parameter observations of motor resistance, inductance, and flux have been achieved [12,13], the rank deficiency problem will cause the estimated value only converge under specific cases.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sensorless model predictive control of permanent magnet synchronous motor based on hybrid parallel observer under parameter uncertainty

Liu,

Zhao,

Zhao

et al. 2024

IET Power Electronics

View full text Add to dashboard Cite

Model predictive control (MPC)‐based permanent magnet synchronous motor (PMSM) drive system has high dependence on parameter accuracy, while the traditional single observer architecture makes it difficult to solve the multi‐parameter observation problem. The authors propose the hybrid observer architecture for MPC in this paper. By constructing an adaptive parameter observer and a sliding mode observer (SMO) working in parallel, the variable exchange mechanism between the observers is designed and the current prediction model is reconstructed. The sensorless control of the PMSM under parameter uncertainty is achieved without affecting the dynamic and steady‐state performance. Compared with the single SMO architecture, the proposed algorithm improves the position identification precision under the uncertainty of the motor resistance and inductance, reducing the dependence of model parameters and improving the performance of sensorless MPC under parameter uncertainty. The experimental results verify the effectiveness of the proposed algorithm.

show abstract

Section: Sensorless Finite Control Set Model Predictive Control Of Pmsmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensorless model predictive control of permanent magnet synchronous motor based on hybrid parallel observer under parameter uncertainty

Liu,

Zhao,

Zhao

et al. 2024

IET Power Electronics

View full text Add to dashboard Cite

show abstract

“…The problem of the parameter identification of the PMSM is quite widely described in the scientific literature, for example [23][24][25][26][27]. In most cases, numerical estimation models such as ARX (autoregressive extra input), ARMA (autoregressive moving average), OE (output error) and others based on experimental data in the time domain are used to estimate parameters of the SPMSM.…”

Section: Introductionmentioning

confidence: 99%

“…There are effective techniques of parameter identification that take into account the influence of back emf and the cross-coupling of currents [23,25,28]. Modified numerical estimation algorithms are proposed in [26,29].…”

Section: Introductionmentioning

confidence: 99%

Modified Technique of Parameter Identification of a Permanent Magnet Synchronous Motor with PWM Inverter in the Presence of Dead-Time Effect and Measurement Noise

et al. 2019

View full text Add to dashboard Cite

The paper considers the problem of parameter identification of the surface mounted permanent magnet synchronous motor (SPMSM) with pulse width modulated (PWM) inverter in the presence of dead time of power switches and other nonlinear distortions. Parameter identification of the SPMSM is required for the tuning of the torque control loop, because in some cases, the exact values of phase resistances and inductances are not known. In the absence of nonlinear disturbances, the problem of SPMSM parameters estimation is not difficult. The influence of the dead-time effect, back electromotive force and measurements noise introduces distortions in experimental output data sets, which leads to incorrect parameter estimation. Thus, there is a need to develop new designs of identification experiments and methods of processing of the experimental data. A detailed mathematical model of SPMSM with a PWM inverter in the presence of dead-time effect is considered in the paper. The negative influence of the dead-time effect on the results of parameter estimation is shown. A modified technique of parameter identification of SPMSM based on the estimation of frequency response function is proposed. The applied design of identification experiments, the type of excitation input signal, and methods of data processing allow us to minimize the influence of nonlinear disturbances and to reduce the variance of estimation of frequency response function. These features provide a high performance of SPMSM parameters estimation.

show abstract

A Parameter Identification Method Based on Forgetting Factor Dynamic Adjustment for PMSM Applied to the Rapid Control of Satellite Attitude

Zhou

2019

J. Electr. Eng. Technol.

View full text Add to dashboard Cite

Online identification for permanent magnet synchronous motor based on recursive fixed memory least square method under steady state

Cited by 12 publications

References 2 publications

Sensorless model predictive control of permanent magnet synchronous motor based on hybrid parallel observer under parameter uncertainty

Sensorless model predictive control of permanent magnet synchronous motor based on hybrid parallel observer under parameter uncertainty

Modified Technique of Parameter Identification of a Permanent Magnet Synchronous Motor with PWM Inverter in the Presence of Dead-Time Effect and Measurement Noise

A Parameter Identification Method Based on Forgetting Factor Dynamic Adjustment for PMSM Applied to the Rapid Control of Satellite Attitude

Contact Info

Product

Resources

About