Adaptive filters in load cell response correction

Shi, W.J.; White, Neil; Brignell, J.E.

doi:10.1016/0924-4247(93)80047-k

Cited by 26 publications

(13 citation statements)

References 2 publications

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“…Previous work has shown that the load cell can be modeled as a second-order system [3] (1) where is the mass being weighed, is the effective mass of the sensor, is the damping factor, is the spring constant, and is the force function. The Laplace transfer function of this sensor is (2) This shows that affects all inertial parameters of the sensor such as gain factor , quality factor , and natural frequency .…”

Section: Load Cell Response Compensationmentioning

confidence: 99%

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Application of Analog Adaptive Filters for Dynamic Sensor Compensation

Jafaripanah

Al-Hashimi

White

2005

IEEE Trans. Instrum. Meas.

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Abstract-This paper investigates the application of analog adaptive techniques to the area of dynamic sensor compensation, of which there is little reported work in the literature. The case is illustrated by showing how the response of a load cell can be improved to speed up the process of measurement. The load cell is a sensor with an oscillatory response in which the measurand contributes to the response parameters. Thus, a compensation filter needs to track variation in measurand, whereas a simple fixed filter is only valid at one specific load value. To facilitate this investigation, computer models for the load cell and the adaptive compensation filter have been developed. To allow a practical implementation of the adaptive techniques, a novel piecewise linearization technique is proposed in order to vary a floating voltage-controlled resistor in a linear manner over a wide range. Simulation and practical results are presented, thus, demonstrating the effectiveness of the proposed techniques.

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Section: Load Cell Response Compensationmentioning

confidence: 99%

“…Software techniques for sensor compensation are reviewed in [2]. Digital adaptive techniques have been used in [3] for load cell response correction. An artificial neural network has been proposed for dynamic measurement which needs a learning phase [4].…”

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confidence: 99%

Application of Analog Adaptive Filters for Dynamic Sensor Compensation

Jafaripanah

Al-Hashimi

White

2005

IEEE Trans. Instrum. Meas.

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“…Due to this control system and signal processing cannot work properly if they collect erroneous data. There are some major drawbacks in current research such as parameter drift, cross-sensitivity, and nonlinearity [1,2].…”

Section: Introductionmentioning

confidence: 99%

“…Thus in some applications, it is demand to operate the measured value in the quick time feasible to speed up the operation of assessment. For example, the sensor output signal is filtering reach response improvement [2]. Imperfection in the sensor output is a widely researched topic and a lot of literatures are present.…”

Section: Introductionmentioning

confidence: 99%

Correction of Load Cell Output using Particle Swarm Optimization

Debnath,

Sarkar,

Chakraborty

et al. 2019

IJEAT

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A load cell is a type of force transducers that transform force and mechanical stress into electrical signal. But the output becomes distorted due to the presence of transient response. Particle Swarm Optimization (PSO) based correction of load cell output is presented this paper. PSO is a robust stochastic optimization technique that considers a swarm of particle (data) as its search space and looks for the best solution. The current approach optimizes a load cell output based on the median value of the signal. The optimization algorithm tries to bring the output response near to the median value.

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“…Identifying the systems transient responses with different inputs and various settling times will take a lot of time and repetition of getting new responses with the maximum overshoot ( p M ) will be harmful for the related systems. Rapid and accurate weighing in industry and its applications has a vast need in laboratory conditions [1,2]. There are various successful researches in speedy weightings and this depends on the filter being used.…”

Section: Introductionmentioning

confidence: 99%

A Non-Linear System’s Response Identification using Artificial Neural Networks

Sokouti

Haghipour

2011

ElAEE

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B. Sokouti, M. Sokouti, S. Haghipour. A Non-Linear System's Response Identification using Artificial Neural Networks // Electronics and Electrical Engineering. -Kaunas: Technologija, 2011. -No. 7(113). -P. 63-66.Identifying the kind of a non-linear system at the first initial times of applying different inputs can be useful n system identification; this identification will get important while the kind of the system's response is being predicted before reaching the %2 range of final value (time delay). As the Artificial neural networks provide the best approximation for non-linear system identification, so in this article the simulation is presented by the means of Radial Basis Function in MATLAB. The presented neural network is successfully able to identify the natural responses of a non-linear system in three modes: under damped, critical damped and over damped. Ill. 5, bibl. 12, tabl. 1 (in English; abstracts in English and Lithuanian). B. Sokouti, M. Sokouti, S. Haghipour. Dirbtinių neuroninių tinklų taikymas atsakymams gauti netiesinėse sistemose // Elektronika ir elektrotechnika. -Kaunas: Technologija, 2011. -Nr. 7(113). -P. 63-66.Pirminis netiesinių sistemų įvertinimas gali būti taikomas signalo vėlinimo trukmei nustatyti. Dirbtiniams neuroniniams tinklams būdinga gera netiesinių sistemų aproksimacija. Pateikiamas radialinės bazinės funkcijos modeliavimas naudojantis programų paketu "Matlab". Il. 5, bibl. 12, lent. 1 (anglų kalba; santraukos anglų ir lietuvių k.).

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Adaptive filters in load cell response correction

Cited by 26 publications

References 2 publications

Application of Analog Adaptive Filters for Dynamic Sensor Compensation

Application of Analog Adaptive Filters for Dynamic Sensor Compensation

Correction of Load Cell Output using Particle Swarm Optimization

A Non-Linear System’s Response Identification using Artificial Neural Networks

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