Advantages of Square-Root Extended Kalman Filter for Sensorless Control of AC Drives

Šmı́dl, Václav; Peroutka, Zdeněk

doi:10.1109/tie.2011.2180273

Cited by 102 publications

(51 citation statements)

References 34 publications

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“…However, some consider that the requirement to superimpose additional AC components to the input signals of the estimator can be overcome by the use of a non-deterministic approach based on Kalman filtering. Model uncertainties and nonlinearities inherent in AC motors as well as signal noises are well suited to the stochastic nature of the EKF, which is basically a recursive observer providing an online state and parameter estimation of a nonlinear dynamical system from noisy measurement signals in a wide speed range [35], [41], [53], [56], [69]. The EKF is also known for its high convergence rate, which significantly improves performance during transients.…”

Section: E New Trendsmentioning

confidence: 99%

“…The computational time consumption, estimation accuracy and instability of the KF are also still open issues. New high level theoretical and applied researches are regularly published, such as [69], [71], [72], and see [11] for survey. Fig.…”

Section: E New Trendsmentioning

confidence: 99%

“…Fig. 4 shows the results of a fixed-point implementation of the EKF used for the sensorless control of a PMSM drives presented in [69]. Three square-root algorithms, namely the Bierman-Thorton, Carlson-Schmidt-Givens, and CarlsonSchmidt-Householder algorithms, have been implemented and compared on both simulation and experimental results.…”

Section: E New Trendsmentioning

confidence: 99%

See 2 more Smart Citations

Industrial Applications of the Kalman Filter: A Review

Auger

Hilairet

Guerrero

et al. 2013

IEEE Trans. Ind. Electron.

499

188

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Abstract-The Kalman filter has received a huge interest from the industrial electronics community and has played a key role in many engineering fields since the 70s, ranging, without being exhaustive, trajectory estimation, state and parameter estimation for control or diagnosis, data merging, signal processing and so on. This paper provides a brief overview of the industrial applications and implementation issues of the Kalman filter in six topics of the industrial electronics community, highlighting some relevant reference papers and giving future research trends.

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Section: E New Trendsmentioning

confidence: 99%

Section: E New Trendsmentioning

confidence: 99%

See 1 more Smart Citation

Industrial Applications of the Kalman Filter: A Review

Auger

Hilairet

Guerrero

et al. 2013

IEEE Trans. Ind. Electron.

499

188

View full text Add to dashboard Cite

show abstract

“…Similarly, its numerical efficiency can be improved by exploiting dedicated factorization techniques which allow notably. (i) sequential measurement processing (Smidl and Peroutka, 2012;Ghanbarpour Asl and Pourtakdoust, 2007), (ii) and scalar calculations (Lu et al, 2007) to be performed in specific cases.…”

Section: Conclusion and Prospectsmentioning

confidence: 99%

Model–Based Techniques for Virtual Sensing of Longitudinal Flight Parameters

Seren

Ezerzere

Hardier

2015

International Journal of Applied Mathematics and Computer Science

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Introduction of fly-by-wire and increasing levels of automation significantly improve the safety of civil aircraft, and result in advanced capabilities for detecting, protecting and optimizing A/C guidance and control. However, this higher complexity requires the availability of some key flight parameters to be extended. Hence, the monitoring and consolidation of those signals is a significant issue, usually achieved via many functionally redundant sensors to extend the way those parameters are measured. This solution penalizes the overall system performance in terms of weight, maintenance, and so on. Other alternatives rely on signal processing or model-based techniques that make a global use of all or part of the sensor data available, supplemented by a model-based simulation of the flight mechanics. That processing achieves real-time estimates of the critical parameters and yields dissimilar signals. Filtered and consolidated information is delivered in unfaulty conditions by estimating an extended state vector, including wind components, and can replace failed signals in degraded conditions. Accordingly, this paper describes two model-based approaches allowing the longitudinal flight parameters of a civil A/C to be estimated on-line. Results are displayed to evaluate the performances in different simulated and real flight conditions, including realistic external disturbances and modeling errors.

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“…최근에는 제곱근 형태와 결합한 칼만 필터 관측기 이론이 전기기 기 제어 분야에 응용되고 있다 [8,9]. Bierman-Thorton, CarlsonSchmidt-Givens 및 Carlson-Schmidt-Householder 등 다양한 제곱 근 확장 칼만 필터를 제안하였으며 [7], Modifed UD factorization을 이용한 무향 칼만 필터는 유도전동기 직접 토크 제어를 위해 제안되 었다 [10].…”

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Parallel Reduced-Order Square-Root Unscented Kalman Filter for State Estimation of Sensorless Permanent-Magnet Synchronous Motor

Moon¹,

Kwon²

2016

The Transactions of The Korean Institute of Electrical Engineer

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-This paper proposes a parallel reduced-order square-root unscented Kalman filter for state estimation of a sensorless permanent-magnet synchronous motor. The appearance of an unscented Kalman filter is caused by the linearization process error between a real system and classical Kalman model. The unscented transformation can make a more accurate Kalman model. However, the complexity is its main drawback. This paper investigates the design and implementation of the proposed filter with Potter and Carlson square-root form. The proposed parallel reduced-order square-root unscented Kalman filter reduces memory and code size, and improves numerical computation. And the performance is not significantly different from the unscented Kalman filter. The experimentation is performed for the verification of the proposed filter.

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Advantages of Square-Root Extended Kalman Filter for Sensorless Control of AC Drives

Cited by 102 publications

References 34 publications

Industrial Applications of the Kalman Filter: A Review

Industrial Applications of the Kalman Filter: A Review

Model–Based Techniques for Virtual Sensing of Longitudinal Flight Parameters

Parallel Reduced-Order Square-Root Unscented Kalman Filter for State Estimation of Sensorless Permanent-Magnet Synchronous Motor

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