Adaptive Kalman filtering for closed-loop systems based on the observation vector covariance

Filho, J. O. A. Limaverde; Fortaleza, Eugênio; Silva, J. G.; Campos, Mário Cesar Mello Massa de

doi:10.1080/00207179.2020.1870158

Cited by 9 publications

(6 citation statements)

References 50 publications

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“…The conditions of the theorem, with the exception of the last one, are well known and provide the existence of the steadystate of the KF. The fulfillment of condition (d) and the restriction on the choice of 𝜇 guarantees the monotonic behavior of the solutions of the matrix equations under consideration (18), (21).…”

Section: Monotonicity Properties Of the Rhofir Filtermentioning

confidence: 99%

“…In general, the Bayesian approach is computationally intractable due to the numerical integration over a large parameter space. In maximum likelihood estimation [19][20][21][22][23][24], the noise statistics are obtained by maximizing the probability density function of the measurement residuals generated by the filter, which is the likelihood of the noise parameters. Adaptive filters based on maximum likelihood methods require nonlinear optimization and are computationally intractable.…”

Section: Introductionmentioning

confidence: 99%

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Finite Impulse Response Filtering Algorithm with Adaptive Horizon Size Selection and Its Applications

Skorohod

2023

Journal of Robotics and Control

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It is known, that unlike the Kalman filter (KF) finite impulse response (FIR) filters allow to avoid the divergence and unsatisfactory object tracking connected with temporary perturbations and abrupt object changes. The main challenge is to provide the appropriate choice of a sliding window size for them. In this paper, the new finite impulse response (FIR) filtering algorithm with the adaptive horizon size selection is proposed. The algorithm uses the receding horizon optimal (RHOFIR) filter which receives estimates, an abrupt change detector and an adaptive recurrent mechanism for choosing the window size. Monotonicity and asymptotic properties of the estimation error covariance matrix and the RHOFIR filter gain are established. These results form a solid foundation for justifying the principal possibility to tune the filter gain using them and the developed adaptation mechanism. The proposed algorithm (the ARHOFIR filter) allows reducing the impact of disturbances by varying adaptively the sliding window size. The possibility of this follows from the fact that the window size affects the filter characteristics in different ways. The ARHOFIR filter chooses a large horizon size in the absence of abrupt disturbances and a little during the time intervals of their action. Due to this, it has better transient characteristics compared to the KF and RHOFIR filter at intervals where there is temporary uncertainty and may provide the same accuracy of estimates as the KF in their absence. By simulation, it is shown that the ARHOFIR filter is more robust than the KF and RHOFIR filter for the temporarily uncertain systems.

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Section: Monotonicity Properties Of the Rhofir Filtermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Finite Impulse Response Filtering Algorithm with Adaptive Horizon Size Selection and Its Applications

Skorohod

2023

Journal of Robotics and Control

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“…However, the satellite clock and receiver clock cannot be fully synchronized within the BeiDou standard time, resulting in time errors. Not only need to solve specific spatial location, to solve the receiver clock is poor, so in the observation point must be at least four visible satellites to according to the equations to solve the specific position [5]. This article only choose four satellites, pseudorange positioning formula is as follows: In the formula, 𝑹 represents the pseudo range from satellite to receiver; (𝑿, 𝒀, 𝒁) represents the satellite spatial position coordinates; (𝒙 𝒖 , 𝒚 𝒖 , 𝒛 𝒖 ) indicates the estimated position of the point to be measured; 𝒄 is the speed of light; 𝜹𝒕 𝒖 is the receiver clock error.…”

Section: Beidou System Pseudorange Positioning Principlementioning

confidence: 99%

Research on location solution based on Kalman filter algorithm

Shao

et al. 2023

International Conference on Signal Processing, Computer Networks, and Communications (SPCNC 2022)

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The increasing perfection of BeiDou positioning system has increased the market of application terminals, but positioning accuracy has become a major problem for users. According to the advantages of Kalman Filtering algorithm, such as time continuity and noise reduction, this paper briefly introduces the principle of pseudo range positioning, introduces, simulates and analyzes the error of least square method and Kalman Filtering method, and proposes a traceless Kalman Filtering algorithm to improve the least square method for adaptive noise estimation. Through MATLAB simulation, the advantages and disadvantages of the algorithm performance are compared, and the feasibility of the algorithm in improving the positioning accuracy is verified.

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“…The adaptive filter, while filtering measured data, concurrently estimates some system model parameters, utilizing limited, indirect, and noisy measurements to infer information that is challenging to measure or ascertain directly [ 30 ]. The adaptive filtering method further encompasses the output error method, innovation method, Sage–Husa method [ 31 ], strong tracking filtering method, and adaptive robust and second-order [ 32 ] mutual difference method [ 31 ], among others. These methods have been effectively employed in the engineering field.…”

Section: Introductionmentioning

confidence: 99%

A Sage–Husa Prediction Algorithm-Based Approach for Correcting the Hall Sensor Position in DC Brushless Motors

Wang

Yong

Yin

2023

Sensors

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Accurate knowledge of the rotor position is essential for the control of brushless DC motors (BLDCM). Any deviation in this identification can cause fluctuations in motor current and torque, increase noise, and lead to reduced motor efficiency. This paper focused on a BLDCM equipped with a three-phase binary Hall sensor. Based on the principle of minimum deviation, this paper estimated the relative installation offset between the Hall sensors. It also provided a clear method for ideal phase commutation position recognition and eliminated the Hall sensor installation position deviation. The proposed pre-calibration method identified and eliminated the offset of the permanent magnet poles, the delay time caused by the Hall signal conditioning circuit, and the offset of the sensor signal identification due to armature response under different loads. Based on the pre-calibration results, a correction strategy for correcting the rotor position information of BLDCMs was proposed. This paper presented a self-adaptive position information prediction algorithm based on the Sage–Husa method. This filters out rotor position information deviations that are not eliminated in pre-calibration. Experimental results on a hydrogen circulation pump motor showed that, after the pre-calibration method was adopted, the Mean Square Error (MSE) of motor speed fluctuations decreased by 92.0%, motor vibration was significantly reduced, average phase current decreased by 62.8%, and the efficiency of the hydrogen circulation pump system was significantly improved. Compared to the traditional KF prediction algorithm, the Sage–Husa adaptive position information prediction algorithm reduced the speed fluctuation during the uniform speed operation stage and speed adjustment stage, the speed curve overshoot, and the commutation time deviation throughout the process by 44.8%, 56.0%, 54.9%, and 14.7%, respectively. This indicates a higher disturbance rejection ability and a more accurate and stable prediction of the commutation moment.

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Adaptive Kalman filtering for closed-loop systems based on the observation vector covariance

Cited by 9 publications

References 50 publications

Finite Impulse Response Filtering Algorithm with Adaptive Horizon Size Selection and Its Applications

Finite Impulse Response Filtering Algorithm with Adaptive Horizon Size Selection and Its Applications

Research on location solution based on Kalman filter algorithm

A Sage–Husa Prediction Algorithm-Based Approach for Correcting the Hall Sensor Position in DC Brushless Motors

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