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Recursive least squares filter

About: Recursive least squares filter is a research topic. Over the lifetime, 8907 publications have been published within this topic receiving 191933 citations.


Papers
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Journal ArticleDOI
TL;DR: The utility of an adaptive method of cancellation of parasitic vibrations for embedded vibration sensing corrupted by extraneous parasitic motion is demonstrated as it uses an adaptive filter that self-tunes to match any unknown phase and gain differences between the SSA and the SM sensor.
Abstract: In this paper, an adaptive method of cancellation of parasitic vibrations is presented for a self-mixing (SM) interferometric laser vibration sensor that has been coupled with a solid-state accelerometer (SSA). Previously, this was achieved using a precalibration of phase and gain mismatches over the complete bandwidth of the instrument. Such a precalibration is not only tedious to execute but also hinders a mass production of the instrument as every SSA–SM sensor couple requires customized calibration. On the other hand, the proposed method does not require any precalibration as it uses an adaptive filter that self-tunes to match any unknown phase and gain differences between the SSA and the SM sensor. Two different adaptive algorithms, namely, recursive least squares (RLS) and least mean squares (LMS) algorithms, are tested and a comparison is established on the basis of parameter dependence, convergence time, computational cost, and rms error. The proposed algorithms have provided improved results (mean errors of 19.1 nm and 20.2 nm for LMS and RLS, respectively) compared with the precalibration-based results (mean error of 24.7 nm) for a laser wavelength of 785 nm. The simulated and experimental results thus demonstrate the utility of such an approach for embedded vibration sensing corrupted by extraneous parasitic motion.

38 citations

Journal ArticleDOI
TL;DR: In this article, a technique for finding the coefficients of a two-dimensional (2D) recursive digital filter having a separable denominator which gives the best approximation, in the least squares sense, to a desired 2-D impulse response over a finite interval is presented.
Abstract: This paper presents a technique for finding the coefficients of a two-dimensional (2-D) recursive digital filter having a separable denominator which gives the best approximation, in the least squares sense, to a desired 2-D impulse response over a finite interval. All the coefficients in the filter are found by iteratively solving linear equations. Since the resulting filter has a separable denominator, it is easy to check the stability and the implementation is simpler. Two examples are given to illustrate the utility of the proposed technique.

38 citations

Journal ArticleDOI
TL;DR: A Lyapunov approach is used to prove both asymptotic stability of estimation error and boundedness in the model parameters suitable for identification of nonlinear dynamic systems.

38 citations

Journal ArticleDOI
17 Feb 2021-Energies
TL;DR: An improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries that can be used to estimate the parameters and the SOC in real time, which does not need to know the state of SOC and the value of open circuit voltage in advance.
Abstract: With the development of new energy vehicle technology, battery management systems used to monitor the state of the battery have been widely researched. The accuracy of the battery status assessment to a great extent depends on the accuracy of the battery model parameters. This paper proposes an improved method for parameter identification and state-of-charge (SOC) estimation for lithium-ion batteries. Using a two-order equivalent circuit model, the battery model is divided into two parts based on fast dynamics and slow dynamics. The recursive least squares method is used to identify parameters of the battery, and then the SOC and the open-circuit voltage of the model is estimated with the extended Kalman filter. The two-module voltages are calculated using estimated open circuit voltage and initial parameters, and model parameters are constantly updated during iteration. The proposed method can be used to estimate the parameters and the SOC in real time, which does not need to know the state of SOC and the value of open circuit voltage in advance. The method is tested using data from dynamic stress tests, the root means squared error of the accuracy of the prediction model is about 0.01 V, and the average SOC estimation error is 0.0139. Results indicate that the method has higher accuracy in offline parameter identification and online state estimation than traditional recursive least squares methods.

38 citations

Journal ArticleDOI
TL;DR: The local approach for detection of abrupt changes is adopted as a computational engine for the change detection and the effectiveness and robustness of the proposed algorithm in fault detection and isolation are demonstrated through Monte Carlo simulations.
Abstract: This paper deals with detection of parameter changes of total least squares and generalized total least squares models and its application in fault detection and isolation. Total least squares and generalized total least squares are frequently used to model processes when all measured process variables are corrupted by disturbances. It is therefore of practical interest to monitor processes and detect faults using the total least squares and generalized total least squares as well. The local approach for detection of abrupt changes is adopted as a computational engine for the change detection. The effectiveness and robustness of the proposed algorithm in fault detection and isolation are demonstrated through Monte Carlo simulations: a pilot-scale experiment and sensor validation of an industrial distillation column.

38 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202356
2022104
2021172
2020228
2019234
2018237