Robust adaptive filtering using recursive weighted least squares with combined scale and variable forgetting factors

Kovačevíc, Branko; Banjac, Zoran; Kovačević, Ivana

doi:10.1186/s13634-016-0341-3

Cited by 13 publications

(16 citation statements)

References 22 publications

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“…To identify the parameter matrix given in (12) (the index p denotes the electrical and mechanical parameters), a weighted recursive least square algorithm is used [35][36][37][38]. λ is defined as the forgetting factor, whose role is to delete past data.…”

Section: Results Of Approachmentioning

confidence: 99%

Parameters estimation of BLDC motor based on physical approach and weighted recursive least square algorithm

Majdoubi

Masmoudi

Bakhti

et al. 2021

IJECE

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Brushless DC motors (BLDCM) are widely used when high precision converters are required. Model based torque control schemes rely on a precise representation of their dynamics, which in turn expect reliable system parameters estimation. In this paper, we propose two procedures for BLDCM parameters identification used in an agriculture mobile robot’s wheel. The first one is based on the physical approach or equations using experimentation data to find the electrical and mechanical parameters of the BLDCM. The parameters are then used to elaborate the model of the motor established in Park’s reference frame. The second procedure is an online identification based on recursive least square algorithm. The procedure is implemented in a closed-loop scheme to guarantee the stability of the system, and it provide parameter matrices obtained by transforming electrical equations, established in Parks reference frame, and mechanical equation to discrete-time domain. From these matrices, and using well formulated intermediate variables, all desired parameters are deduced simultaneously. The identification procedures are being verified using simulation under Matlab-Simulink software.

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Section: Results Of Approachmentioning

confidence: 99%

Parameters estimation of BLDC motor based on physical approach and weighted recursive least square algorithm

Majdoubi

Masmoudi

Bakhti

et al. 2021

IJECE

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show abstract

“…However, in these RLS algorithms with constant FFs, a large FF close to one reduces the convergence speed while a small FF close to zero leads to large misadjustment [20]. In [20], [21], RLS algorithms with variable FFs have been developed to attain the minimal misadjustment as well as to estimate the optimal FF. Estimation lag, however, is inevitable in these FF-based recursive algorithms since they use previous information to estimate current parameters.…”

Section: ) Limited Tracking Speed Of Existing Recursive Algorithmsmentioning

confidence: 99%

“…There exists inherently numerical instability in RLS-like algorithms [8], [17], [19]- [21], [25], [28]. The covariance matrix of input signals may become poorly conditioned or even singular if input signals are not persistently exciting [30].…”

Section: ) Numerical Instability Of Existing Covariance-matrix-basedmentioning

confidence: 99%

Enhancing Accuracy and Numerical Stability for Repetitive Time-Varying System Identification: An Iterative Learning Approach

et al. 2020

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Time-varying system identification is an appealing but challenging research area. Existing identification algorithms are usually subject to either low estimation accuracy or bad numerical stability. These deficiencies motivate the development of an iterative learning identification algorithm in this paper. Three distinguished features of the proposed method result in the achievement of high estimation accuracy and high numerical stability: i) recursion along the iteration axis, ii) bias compensation, and iii) singular value decomposition (SVD). Firstly, an extra iteration axis associated with the original time axis is introduced in the parameter estimation process. A norm-optimal identification approach with the balance between convergence speed and noise robustness is then proposed along the iteration axis, followed by further analysis on the accuracy and the numerical stability. Secondly, in order to eliminate the estimation bias in the presence of noise and thus to improve the accuracy, a bias compensation algorithm along the iteration axis is proposed. Thirdly, a SVD-based update algorithm for the covariance matrix is developed to avoid the possible numerical instability during iterations. Numerical examples are finally provided to validate the algorithm and confirm its effectiveness.

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“…At the start of the current trial, samples in the sliding-window were mostly occupied by data from the previous trial. In such a case,β was affected by previous CBF activities, leading to a false estimation.β would gradually converge to the true value with the transition of k. To overcome this weakness, an enhanced, faster convergence method, for example, by variable forgetting factors or variable-length sliding window analysis, should be utilized [34][35][36][37].…”

Section: Limitationsmentioning

confidence: 99%

Real-Time Reduction of Task-Related Scalp-Hemodynamics Artifact in Functional Near-Infrared Spectroscopy with Sliding-Window Analysis

et al. 2018

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Functional near-infrared spectroscopy (fNIRS) is an effective non-invasive neuroimaging technique for measuring hemoglobin concentration in the cerebral cortex. Owing to the nature of fNIRS measurement principles, measured signals can be contaminated with task-related scalp blood flow (SBF), which is distributed over the whole head and masks true brain activity. Aiming for fNIRS-based real-time application, we proposed a real-time task-related SBF artifact reduction method. Using a principal component analysis, we estimated a global temporal pattern of SBF from few short-channels, then we applied a general linear model for removing it from long-channels that were possibly contaminated by SBF. Sliding-window analysis was applied for both signal steps for real-time processing. To assess the performance, a semi-real simulation was executed with measured short-channel signals in a motor-task experiment. Compared with conventional techniques with no elements of SBF, the proposed method showed significantly higher estimation performance for true brain activation under a task-related SBF artifact environment.

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Robust adaptive filtering using recursive weighted least squares with combined scale and variable forgetting factors

Cited by 13 publications

References 22 publications

Parameters estimation of BLDC motor based on physical approach and weighted recursive least square algorithm

Parameters estimation of BLDC motor based on physical approach and weighted recursive least square algorithm

Enhancing Accuracy and Numerical Stability for Repetitive Time-Varying System Identification: An Iterative Learning Approach

Real-Time Reduction of Task-Related Scalp-Hemodynamics Artifact in Functional Near-Infrared Spectroscopy with Sliding-Window Analysis

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