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

Zerdali, Emrah; Yıldız, Recep; İnan, Remzi; Demir, Rıdvan; Barut, Murat

doi:10.1002/2050-7038.12684

Cited by 13 publications

(14 citation statements)

References 30 publications

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“…Theorem 1. Given the robot system (25), measurement (27) and the AFEKF algorithm shown in Definition 1, the localization error ( 29) is exponentially bounded in mean square and bounded with probability one under the following initial condition:…”

Section: Stability Analysismentioning

confidence: 99%

“…For nonlinear Gaussian systems, the corresponding AFEKF algorithm has been developed under the conventional EKF frame and some early research interests have been reported [23][24][25]. Moreover, a few practical engineering problems have been solved by utilizing such an adaptive algorithm [26,27]. For example, in Reference [26], a multiple fading factors-based adaptive H ∞ KF algorithm has been proposed for the unmanned underwater vehicle navigation.…”

Section: Introductionmentioning

confidence: 99%

“…For example, in Reference [26], a multiple fading factors-based adaptive H ∞ KF algorithm has been proposed for the unmanned underwater vehicle navigation. In Reference [27], the speed and the load torque estimation problem has been investigated for induction motors by using the AFEKF. Until now, the localization problem of applying the AFEKF algorithm to constrain the impact of unknown modeling errors is still open, and this composes the principal focus of our present study.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Fading Extended Kalman Filtering for Mobile Robot Localization Using a Doppler–Azimuth Radar

Bin

Karimi

2021

Electronics

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In this paper, the localization problem of a mobile robot equipped with a Doppler–azimuth radar (D–AR) is investigated in the environment with multiple landmarks. For the type (2,0) robot kinematic model, the unknown modeling errors are generally aroused by the inaccurate odometer measurement. Meanwhile, the inaccurate odometer measurement can also give rise to a type of unknown bias for the D–AR measurement. For reducing the influence induced by modeling errors on the localization performance and enhancing the practicability of the developed robot localization algorithm, an adaptive fading extended Kalman filter (AFEKF)-based robot localization scheme is proposed. First, the robot kinematic model and the D–AR measurement model are modified by considering the impact caused by the inaccurate odometer measurement. Subsequently, in the frame of adaptive fading extended Kalman filtering, the way to the addressed robot localization problem with unknown biases is sought out and the stability of the developed AFEKF-based localization algorithm is also discussed. Finally, in order to testify the feasibility of the AFEKF-based localization scheme, three different kinds of modeling errors are considered and the comparative simulations are conducted with the conventional EKF. From the comparative simulation results, it can be seen that the average localization error under the developed AFEKF-based localization scheme is [0.0245m0.0224m0.0039rad]T and the average localization errors using the conventional EKF are [1.0405m2.2700m0.1782rad]T, [0.4963m0.3482m0.0254rad]T and [0.2774m0.3897m0.0353rad]T, respectively, under the three cases of the constant bias, the white Gaussian stochastic bias and the bounded uncertainty bias.

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Section: Stability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive Fading Extended Kalman Filtering for Mobile Robot Localization Using a Doppler–Azimuth Radar

Bin

Karimi

2021

Electronics

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“…In theory, the fading filter is another kind of adaptive filter, and it can control the influences of the dynamic model errors [17]. The most important problem for the fading filter is to construct a suitable factor S k .…”

Section: Introductionmentioning

confidence: 99%

Modified Adaptive Fusion Scheme for Kalman Filter Based on the Hypothesis Test

Jiang

Liu

et al. 2022

Journal of Sensors

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In the literature, the fading factor was constructed to overcome the shortage of model uncertainties in the Kalman filter. However, the a priori covariance matrix might be inflated abnormally by the fading factor once the measurement is unreliable. Thus, the fading factor may become invalid, and this problem is rarely discussed and tested. In this paper, squares of the Mahalanobis distance are introduced as the judging index, and the fading factor or the covariance inflation factor is adopted conditionally according to the hypothesis testing result. Therefore, an adaptive filtering scheme based on the Mahalanobis distance is put forward for the systems with model uncertainties. The proposed algorithm is implemented with the actual data collected by the integration of the global navigation satellite system (GNSS) and the inertial navigation system and INS (inertial navigation system) integrated systems (INS). For the systems with model uncertainties, experimental results demonstrate that the influences of both the outlying measurements and model errors are controlled effectively with the proposed scheme.

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“…However, it needs parameter tuning efforts. In order to compensate for the effect of the incomplete dynamic equation for the estimations of induction motor parameters, Zerdali [25] proposes designing an adaptive fading EKF (AFEKF) observer. To show the superiority of AFEKF, its estimation performance is compared to that of standard EKF methods, especially in transient states.…”

Section: Introductionmentioning

confidence: 99%

MPPT Improvement for PMSG-Based Wind Turbines Using Extended Kalman Filter and Fuzzy Control System

et al. 2021

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Variable speed wind turbines are commonly used as wind power generation systems because of their lower maintenance cost and flexible speed control. The optimum output power for a wind turbine can be extracted using maximum power point tracking (MPPT) strategies. However, unpredictable parameters, such as wind speed and air density could affect the accuracy of the MPPT methods, especially during the wind speed small oscillations. In this paper, in a permanent magnet synchronous generator (PMSG), the MPPT is implemented by determining the uncertainty of the unpredictable parameters using the extended Kalman filter (EKF). Also, the generator speed is controlled by employing a fuzzy logic control (FLC) system. This study aims at minimizing the effects of unpredictable parameters on the MPPT of the PMSG system. The simulation results represent an improvement in MPPT accuracy and output power efficiency.

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

Cited by 13 publications

References 30 publications

Adaptive Fading Extended Kalman Filtering for Mobile Robot Localization Using a Doppler–Azimuth Radar

Adaptive Fading Extended Kalman Filtering for Mobile Robot Localization Using a Doppler–Azimuth Radar

Modified Adaptive Fusion Scheme for Kalman Filter Based on the Hypothesis Test

MPPT Improvement for PMSG-Based Wind Turbines Using Extended Kalman Filter and Fuzzy Control System

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