Sensorless Vector Control of an IPMSM using Unscented Kalman Filtering

Ndjana, Herve Joel Nanga; Lautier, Ph.

doi:10.1109/isie.2006.295921

Cited by 17 publications

(5 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Section: A Ukfmentioning

confidence: 99%

“…Application of UKF in PMSM sensorless control is reported in [72]- [77]. The speed and position estimation based on fourth-order UKF is studied in [72]- [74], and fifth-order UKF considering disturbance variable is developed in [75]. Furthermore, the improvements of UKF such as adaptive UKF [76], improved UKF [77], and neural network aided UKF [78] are reported in recent years.…”

Section: A Ukfmentioning

confidence: 99%

See 1 more Smart Citation

A review of nonlinear Kalman filter appling to sensorless control for AC motor drives

Gao

Zhang

et al. 2019

Trans. Electr. Mach. Syst.

View full text Add to dashboard Cite

Section: A Ukfmentioning

confidence: 99%

Section: A Ukfmentioning

confidence: 99%

A review of nonlinear Kalman filter appling to sensorless control for AC motor drives

Gao

Zhang

et al. 2019

Trans. Electr. Mach. Syst.

View full text Add to dashboard Cite

“…To demonstrate the performance of the proposed control scheme, a set of computer simulation runs is carried out on an Interior Permanent Magnet Synchronous Motor (IPMSM) model, which has been validated experimentally [20] and has been used to design industrial controllers for PMSMbased products, such as, hydroelectric and wind turbines. Table I summarizes the motor's parameters along with their respective values.…”

Section: A Setupmentioning

confidence: 99%

Robust ANN-based nonlinear speed observer for permanent magnet synchronous machine drives

Chaoui

Sicard

2011

2011 IEEE International Electric Machines &Amp; Drives Conference (IEMDC)

View full text Add to dashboard Cite

This paper introduces a robust artificial neural network (ANN) based nonlinear speed observer for permanent magnet synchronous machines (PMSMs). A multilayer perception is trained online using back-propagation learning algorithm to estimate the rotor speed without any a priori dynamics knowledge. Thus, the proposed observer is able to cope with higher degrees of nonlinearity since it is not based on a linear-in-parameters model, unlike many neural network observers. Therefore, robustness to parameter variations is achieved. Simulation results for different situations highlight the performance of the proposed observer in the presence of high parametric uncertainties. The proposed observer is reliable and effective for PMSM drives.

show abstract

“…To demonstrate the performance of the proposed control scheme, a set of computer simulation runs is carried out on an Interior Permanent Magnet Synchronous Motor (IPMSM) model, which has been validated experimentally [19], and it has been used to design industrial controllers for PMSM-based products such as, hydroelectric, and wind turbines. Table 1 summarizes the machine's parameters along with their respective values.…”

Section: Setupmentioning

confidence: 99%

Adaptive Lyapunov-based neural network sensorless control of permanent magnet synchronous machines

Chaoui

Sicard

2010

Neural Comput & Applic

View full text Add to dashboard Cite

In this paper, an adaptive neural network sensorless control scheme is introduced for permanent magnet synchronous machines (PMSMs). The control strategy consists of an adaptive speed controller that capitalizes on the machine's inverse model to achieve accurate tracking, two artificial neural networks (ANNs) for currents control, and an ANN-based observer for speed estimation to overcome the drawback associated with the use of mechanical sensors while the rotor position is obtained by the estimated rotor speed direct integration to reduce the effect of the system noise. A Lyapunov stability-based ANN learning technique is also proposed to insure the ANNs' convergence and stability. Unlike other sensorless control strategies, no a priori offline training, weights initialization, voltage transducer, or mechanical parameters knowledge is required. Results for different situations highlight the performance of the proposed controller in transient, steady-state, and standstill conditions.

show abstract

Sensorless Vector Control of an IPMSM using Unscented Kalman Filtering

Cited by 17 publications

References 9 publications

A review of nonlinear Kalman filter appling to sensorless control for AC motor drives

A review of nonlinear Kalman filter appling to sensorless control for AC motor drives

Robust ANN-based nonlinear speed observer for permanent magnet synchronous machine drives

Adaptive Lyapunov-based neural network sensorless control of permanent magnet synchronous machines

Contact Info

Product

Resources

About