Dynamic Compensation of Piezoresistive Pressure Sensor Based on Sparse Domain

Xu, Bo; Han, Tong; Liu, Hong; Wang, Xiao; Ju, Mingchi

doi:10.1155/2020/8890028

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…Accordingly, the selection, crossover, and mutation operations are carried out. The fitness function is shown in equation (11).…”

Section: Analysis and Discussion Of 3d Force Prediction Resultsmentioning

confidence: 99%

“…In equation (11), F m i ði = x, y, zÞ denotes the expected outputs of 3D force components for the mth sample, while 3.…”

Section: Analysis and Discussion Of 3d Force Prediction Resultsmentioning

confidence: 99%

“…Tactile sensors have become one of the hot research topics in the robot field, and tactile sensors which could imitate the human skin play a key role in the intelligent robot area. According to the working principles, tactile sensors could be categorized into several groups such as piezoelectric sensors [1][2][3][4][5], capacitive sensors [6][7][8][9][10], piezoresistive sensors [11][12][13], and electromagnetic sensors [14][15][16]. A lot of researchers focused on the materials and the structures of the tactile sensors, and the research of multidimensional and multifunctional flexible tactile sensors based on neural network methods has gradually become the focus in electronic skin field.…”

Section: Introductionmentioning

confidence: 99%

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Modeling Analysis and 3D Force Prediction of a Novel Piezoelectric Tactile Sensor

Wang

Yang

et al. 2021

Journal of Sensors

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To measure three-dimensional (3D) forces efficiently and improve the sensitivity of tactile sensors, a novel piezoelectric tactile sensor with a “sandwich” structure is proposed in this paper. An array of circular truncated cone-shaped sensitive units made of polyvinylidene fluoride (PVDF) is sandwiched between two flexible substrates of polydimethylsiloxane (PDMS). Based on the piezoelectric properties of the PVD F sensitive units, finite element modelling and analysis are carried out for the sensor. The relation between the force and the voltage of the sensitive unit is obtained, and a tactile perception model is established. The model can distinguish the sliding direction and identify the material of the slider loaded on the sensor. A backpropagation neural network (BPNN) algorithm is built to predict the 3D forces applied on the tactile sensor model, and the 3D forces are decoupled from the voltages of the sensitive units. The BPNN is further optimized by a genetic algorithm (GA) to improve the accuracy of the 3D force prediction, and fairly good prediction results are obtained. The experimental results show that the novel tactile sensor model can effectively predict the 3D forces, and the BPNN model optimized by the GA can predict the 3D forces with much higher precision, which also improves the intelligence of the sensor. All the prediction results indicate that the BPNN algorithm has very efficient performance in 3D force prediction for the piezoelectric tactile sensor.

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“…Accordingly, the selection, crossover, and mutation operations are carried out. The fitness function is shown in equation (11).…”

Section: Analysis and Discussion Of 3d Force Prediction Resultsmentioning

confidence: 99%

“…In equation (11), F m i ði = x, y, zÞ denotes the expected outputs of 3D force components for the mth sample, while 3.…”

Section: Analysis and Discussion Of 3d Force Prediction Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling Analysis and 3D Force Prediction of a Novel Piezoelectric Tactile Sensor

Wang

Yang

et al. 2021

Journal of Sensors

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“…The extensive literature search resulted in some publications correlated with the subject of this paper. The correction of dynamical properties of data acquisition systems and sensors is obtained there by inverse modelling [ 2 , 3 , 4 , 5 , 6 , 7 , 8 ], or inverse modelling aided by a feedback control system [ 9 ], or one-step forward specialised prediction [ 10 , 11 , 12 ], or joint input and state estimation based on Kalman filtering [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. All these correction methods use linear discrete-time dynamic models of data acquisition systems.…”

Section: Introductionmentioning

confidence: 99%

Correction of Dynamical Properties of Data Acquisition Systems

Figwer

Michalczyk

2023

Sensors

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Accurate and fast measurements are important in many areas of everyday engineering and research activities. This paper proposes a method that gives such measurements, additionally shortening the time in which they are obtained. To achieve this, a supplementary discrete-time filter, estimating values of delayed samples of the measured signal, is attached to the output of the data acquisition system. This filter is identified with the use of classical estimation methods, based on a dynamical model of the data acquisition system. The definition of the cost function minimised during filter identification depends on the nature of the environment in which measurements are acquired. The considerations presented in this paper are illustrated with four corresponding simulated case study examples. They show how, in a very simple way, dynamical properties of data acquisition systems may be corrected, and thus improved, using the corresponding supplementary discrete-time filters. The improvement, measured by the correction quality index introduced in the paper, was from a few times up to more than 100. The paper also raises the issue of obtaining models for tuning of the supplementary discrete-time filter. The considerations presented may be applied to formulate the artificial intelligence of data acquisition systems as well as sensors. Finally, the paper proposes to implement this intelligence as a knowledge base of the expert system.

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