Extended Kalman Filter for High performances Sensorless Induction Motor drive

Et-Taaj, L.; Boulghasoul, Z.; Elkharki, A.; Kandoussi, Zineb; Elbacha, Abdelhadi

doi:10.1109/icocs.2019.8930736

Cited by 5 publications

(4 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This non-constant speed results in a nonlinear system, which in turn requires the utilization of nonlinear state estimation techniques. To the best of our knowledge, the Extended Kalman Filter is the most widely used technique for state estimation in induction machines, see, e.g., [30][31][32][33][34]. Additionally, the Euler method is typically used for the attainment of discrete-time induction machine models, and whilst at high sampling rates it can yield adequate models, our work suggests that higher-order methods yield more accurate models that result in more accurate state estimation in terms of root mean square error.…”

Section: Introductionmentioning

confidence: 99%

“…Historically, in machine drives, the Euler method was widely adopted in the literature to obtain a discrete-time model of the machine due to its simplicity, see, e.g., [41][42][43]. In fact, in Bayesian filtering applications on IM drives using EKF [27,[32][33][34] and UKF [35,[37][38][39][40], Euler discretization methods are typically considered. Higher-order methods like the Taylor or Runge-Kutta, which involve greater computational costs and usually yield more complex systems, are not commonly utilized.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On Accurate Discrete-Time Dynamic Models of an Induction Machine

Herrera Hernández,

Reusser,

Coronel

et al. 2024

Mathematics

View full text Add to dashboard Cite

Induction machines have become the standard for highly demanding industrial applications. This has led to the utilization of modern discrete-time control techniques (such as model predictive control) that require the estimation of internal variables that are not subject to measurement (such as the rotational velocity in sensorless applications). From this point of view, it is fundamental to have accurate discrete-time models of induction machines, particularly given their nonlinear nature, so that control techniques perform according to design requirements. In spite of the above, the modeling of induction machines has not received much attention in the literature, even though more powerful machines and faster microcontrollers are currently being used. To better understand induction machine models for control, in this paper, we develop and compare various discrete-time models of induction machines based on Euler, Taylor, and Runge–Kutta methods. In addition, we compare the Extended Kalman Filter and Unscented Kalman Filter for state estimation in terms of accuracy and computational burden. The models are derived and compared through extensive Monte Carlo simulations and the state estimation techniques are compared in terms of root mean squared error, execution time, and maximum absolute error. Our simulations show that, in general, the Taylor method yields more accurate models than both the Runge–Kutta and Euler methods. In particular, the Taylor method results in a root mean square error that is one order of magnitude smaller than the Euler method for stator current and rotor flux linkages. For rotor angular speed, the Runge–Kutta methods are more accurate than both the Taylor and Euler methods, resulting in a root mean square error that is two orders of magnitude smaller than the Euler method. On the other hand, the Extended Kalman Filter results in smaller execution time than the Unscented Kalman Filter, up to two orders of magnitude. In terms of root mean squared error and maximum absolute error, both filtering algorithms perform similarly.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On Accurate Discrete-Time Dynamic Models of an Induction Machine

Herrera Hernández,

Reusser,

Coronel

et al. 2024

Mathematics

View full text Add to dashboard Cite

show abstract

“…Indirect Field oriented control (IFOC) Vector-control drives are preferred over scalar-controlled drives due to higher accuracy and better stability [7]. Later, speed Sensorless vector-controlled IM drives are very popular for higher-performance applications [8], [9]. Among various techniques, model-based speed estimation techniques are providing flexibility to implement easily with higher dynamic performance.…”

mentioning

confidence: 99%

“…Each of the methods has its merit and demerits. These techniques are named; Model reference adaptive system (MRAS), Sliding mode observer (SMO), and other Artificial Intelligence (AI) techniques, Extended Kalman filter (EKF), Extended Luenberger observer (ELO) [8], [10]- [14].…”

mentioning

confidence: 99%

Performance Analysis of Sensorless Induction Motor Drive using Improved Control Techniques

Yadav,

Bhasme

2023

IJEER

View full text Add to dashboard Cite

AC Drives demand robust motor design with rugged construction, low cost, high reliability in service, and simple maintenance. In modern power drives, Sensorless Induction motor drives are more popular than other drives. A speed sensor/encoder-based drive is costlier and requires more space in case more parallel units are coupled together in drive operation. To address these difficulties, speed sensorless drives are introduced without loss of efficiency and reliability. However, Sensorless speed drive requires advanced control techniques in which complex calculations are there due to the nonlinearity of IM. In the recent past, Machine model-based methods i.e., MRAS (Model reference adaptive systems) and ELO (Extended Luenberger observer) have been popularized due to ease of implementation compared to other techniques. In this work, improved MRAS and ELO models are implemented and verified using Simulink software. Then, the same is validated with the help of an FPGA (field programmable gate array) based Snetly real-time controller. The key improvements are achieved as follows: effective speed estimation, robust speed tracking, reduced speed estimation error, and robust stability which helps to enhance the transient and steady-state performance of the drive.

show abstract

Performance Analysis of EKF-based Sensorless Induction Motor Drive using FPGA Controller

Maddu

Bhasme²

2023

2023 13th International Symposium on Advanced Topics in Electrical Engineering (ATEE)

View full text Add to dashboard Cite

Extended Kalman Filter for High performances Sensorless Induction Motor drive

Cited by 5 publications

References 18 publications

On Accurate Discrete-Time Dynamic Models of an Induction Machine

On Accurate Discrete-Time Dynamic Models of an Induction Machine

Performance Analysis of Sensorless Induction Motor Drive using Improved Control Techniques

Performance Analysis of EKF-based Sensorless Induction Motor Drive using FPGA Controller

Contact Info

Product

Resources

About