A Connection Between the Kalman Filter and an Optimized LMS Algorithm for Bilinear Forms

Dogariu, Laura-Maria; Ciochină, Silviu; Paleologu, Constantin; Benesty, Jacob

doi:10.3390/a11120211

Cited by 7 publications

(6 citation statements)

References 27 publications

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“…As shown in Equation (18), the frequency bandwidth of the controller is given by ω c . Therefore, it is possible to design a controller without overshoot only by determining the frequency bandwidth of the desired controller.…”

Section: Tension Controllermentioning

confidence: 99%

“…Moreover, a high-price load cell with a highly accurate and noiseless signal should be used to improve tension control performance in the R2R system. In order to reduce costs, which is an unavoidable issue in today's industry, the KF algorithm was suggested for signal processing [15][16][17][18]. Almost all load cell applications require filtering to remove noise from the measured signal.…”

Section: Introductionmentioning

confidence: 99%

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Kalman-Filter-Based Tension Control Design for Industrial Roll-to-Roll System

Hwang¹,

Lee²,

Eum³

et al. 2019

Algorithms

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This paper presents a robust and precise tension control method for a roll-to-roll (R2R) system. In R2R processing, robust and precise tension control is very important because improper web tension control leads to deterioration in the quality of web material. However, tension control is not easy because the R2R system has a model variation in which the inertia of the web in roll form is changed and external disturbances caused by web slip and crumpled web. Therefore, a disturbance observer (DOB) was proposed to achieve robustness against model variations and external disturbances. DOB is a robust control method widely used in various fields because of its simple structure and excellent performance. Moreover, the web passes through various process steps to achieve the finished product in the R2R process. Particularly, it is important to track the tension when magnitude of the tension varies during process. Feedforward (FF) controller was applied to minimize the tracking error in the transient section where tension changes. Moreover, the signal processing of a sensor using the Kalman filter (KF) in the R2R system greatly improved control performance. Finally, the effectiveness of the proposed control scheme is discussed using experimental results.

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Section: Tension Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Kalman-Filter-Based Tension Control Design for Industrial Roll-to-Roll System

Hwang¹,

Lee²,

Eum³

et al. 2019

Algorithms

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show abstract

“…These algorithms have improved convergence features as compared to their LMS-based counterparts. Moreover, a Kalman filter tailored to the identification of bilinear forms was analyzed in [27,28].…”

Section: Introductionmentioning

confidence: 99%

A Kalman Filter for Multilinear Forms and Its Connection with Tensorial Adaptive Filters

Dogariu

Paleologu

Benesty

et al. 2021

Sensors

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The Kalman filter represents a very popular signal processing tool, with a wide range of applications within many fields. Following a Bayesian framework, the Kalman filter recursively provides an optimal estimate of a set of unknown variables based on a set of noisy observations. Therefore, it fits system identification problems very well. Nevertheless, such scenarios become more challenging (in terms of the convergence and accuracy of the solution) when the parameter space becomes larger. In this context, the identification of linearly separable systems can be efficiently addressed by exploiting tensor-based decomposition techniques. Such multilinear forms can be modeled as rank-1 tensors, while the final solution is obtained by solving and combining low-dimension system identification problems related to the individual components of the tensor. Recently, the identification of multilinear forms was addressed based on the Wiener filter and most well-known adaptive algorithms. In this work, we propose a tensorial Kalman filter tailored to the identification of multilinear forms. Furthermore, we also show the connection between the proposed algorithm and other tensor-based adaptive filters. Simulation results support the theoretical findings and show the appealing performance features of the proposed Kalman filter for multilinear forms.

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“…The measurement noise is taken to be known. An Adaptation of these algorithms for bilinear forms is described in [18 ], along with a comparison with the KF for the same problem. Finally, Sulthana et al .…”

Section: Introductionmentioning

confidence: 99%

Bayesian step least mean squares algorithm for Gaussian signals

Lopes¹

2020

IET signal process.

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A Connection Between the Kalman Filter and an Optimized LMS Algorithm for Bilinear Forms

Cited by 7 publications

References 27 publications

Kalman-Filter-Based Tension Control Design for Industrial Roll-to-Roll System

Kalman-Filter-Based Tension Control Design for Industrial Roll-to-Roll System

A Kalman Filter for Multilinear Forms and Its Connection with Tensorial Adaptive Filters

Bayesian step least mean squares algorithm for Gaussian signals

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