A Comprehensive Comparison of Extended and Unscented Kalman Filters for Speed-Sensorless Control Applications of Induction Motors

Yıldız, Recep; Barut, Murat; Zerdali, Emrah

doi:10.1109/tii.2020.2964876

Cited by 74 publications

(42 citation statements)

References 25 publications

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“…On the other hand, eliminating the speed-sensor in electric drive systems, namely speed-sensorless control, reduce cost, hardware complexity, and maintenance requirements while increasing the reliability of the electric drive system. For this purpose, many model-based estimators/observers have been proposed in the literature, such as model reference adaptive systems [19,20], full-order observers [21,22], extended Luenberger observers [23,24], extended and unscented Kalman filters (EKF and UKF) [25][26][27], and sliding mode observers [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

“…Unlike the other methods, nonlinear Kalman filtering (NKF) methods (i.e., EKF and UKF) provide a stochastic approach to state/parameter estimation problem by taking into account the process and measurement noises. A very recent study [27] in which EKF and UKF observers are compared for speed-sensorless control applications of IM states that EKF is still the best option with low computational complexity and an estimation performance similar to UKF. However, NKF methods require a stochastic system with complete dynamic and measurement equations to perform optimal estimations, and in many practical applications, those are either unknown or partially known [31].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Speed-sensorless predictive torque controlled induction motor drive with feed-forward control of load torque for electric vehicle applications

Zerdali¹,

Demir²

2021

Turk J Elec Eng & Comp Sci

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Nowadays, the global trend is towards reducing CO2 emissions and one solution is to replace internal combustion vehicles with electric vehicles. To this end, electric drive system, the most crucial part of an electric vehicle, has gained importance and has become a major research field. The induction motor (IM) is one of the best candidates for electric vehicle applications due to its advantages such as simple and robust design, low cost and maintenance requirements, the ability to operate in harsh environments. However, it has a highly nonlinear model with time-varying electrical and mechanical parameters making them difficult to control. Finite control set-predictive torque control (FCS-PTC) is an inherently suitable and a promising control method for the IM because FCS-PTC is easy to implement and has the ability to handle nonlinearities with the inclusion of constraints. In addition, the elimination of speed sensors increases the reliability of electric motor drives while reducing cost and hardware complexity. In this paper, a speedsensorless FCS-PTC based IM drive system is designed in order to combine the aforementioned advantages. Unlike the current literature, to improve the torque response of conventional FCS-PTC, the load torque is also estimated by an adaptive fading extended Kalman filter and is fed back into the torque control loop. The results show that improved control performance is achieved.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Speed-sensorless predictive torque controlled induction motor drive with feed-forward control of load torque for electric vehicle applications

Zerdali¹,

Demir²

2021

Turk J Elec Eng & Comp Sci

Self Cite

View full text Add to dashboard Cite

show abstract

“…Speed, load torque, and efficiency are estimated by an IM model-based EKF [31]. Stator currents, rotor fluxes, load including viscous friction are estimated using EKF and UKF algorithms [32]. In the paper, primitive KF is utilized for the filtration of stator current vector components in case of unknown IM model.…”

Section: Introductionmentioning

confidence: 99%

Pulse-width modulation direct torque control induction motor drive with Kalman filter

Nguyen

et al. 2021

TELKOMNIKA

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The paper deals with application of Kalman filter in induction motor drive using pulse-width modulation direct torque control (PWM-DTC). In the first part, the conventional PWM-DTC drive is described and Kalman filter is utilized to filter components of stator current vector those are assumed to be disturbed by white noise. The second part contains simulation results that are obtained in different cases of load torque, process and measurement noise covariances. The integral time absolute error (ITAE) performance index, undershoot, ripple of important quantities are used to compare the conventional drive structure and proposed drive structure with Kalman filter. The simulation results confirm the expected dynamic response of the proposed structure.

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“…Model‐based methods using two‐phase IM models are the most popular methods thanks to their easy implementation and satisfactory performance apart from extra‐low speeds. Hence, different model‐based methods such as model reference adaptive systems, 2,3 full‐order observers, 4,5 sliding mode observers, 6,7 nonlinear Luenberger observers, 8,9 and nonlinear Kalman filters 10,11 have been introduced. Although all those methods are based on the machine model, they can also be classified as deterministic 2‐9 and stochastic 10,11 approaches.…”

Section: Introductionmentioning

confidence: 99%

“…In practice, Q and R may vary depending on the operating conditions; therefore, they should be updated online for further improvement in estimation performance. In addition, Yildiz et al 10 make a comprehensive comparison of the most popular two NKFs, extended and unscented Kalman filters (EKF and UKF), for speed‐sensorless control applications. Authors emphasize that the computational load of UKF is about six times higher than that of EKF although their estimation performances are very similar.…”

Section: Introductionmentioning

confidence: 99%

Improved speed and load torque estimations with adaptive fading extended Kalman filter

Zerdali

Yıldız

İnan

et al. 2020

Int Trans Electr Energ Syst

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A Comprehensive Comparison of Extended and Unscented Kalman Filters for Speed-Sensorless Control Applications of Induction Motors

Cited by 74 publications

References 25 publications

Speed-sensorless predictive torque controlled induction motor drive with feed-forward control of load torque for electric vehicle applications

Speed-sensorless predictive torque controlled induction motor drive with feed-forward control of load torque for electric vehicle applications

Pulse-width modulation direct torque control induction motor drive with Kalman filter

Improved speed and load torque estimations with adaptive fading extended Kalman filter

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