Non-linearity Estimation and Temperature Compensation of Capacitor Pressure Sensors Using Least Square Support Vector Regression

Wang, Xiaoh

doi:10.1109/kamw.2008.4810702

Cited by 7 publications

(5 citation statements)

References 8 publications

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“…Newer techniques, such as Locally-Weighted Projection Regression (LWPR) have been used in force control applications [11]; however, like NNs, LWPR typically requires optimal tuning of numerous hyperparameters. Kernelbased support vector machines (SVM) have been employed to fit nonlinear functions to cross-axis coupling terms in multiaxis strain gage-based force sensors [12] and to compensate for nonlinear and environmental effects in photoelectric displacement sensors [13] and capacitive pressure sensors [14]. To the author's knowledge, there exists no prior literature in applying kernel-based machine learning techniques to actively reject thermal and ambient light disturbances while approximating cross-axis coupling relationships for optoelectronic multi-axis force sensing methodologies.…”

Section: Introductionmentioning

confidence: 99%

Machine learning approaches to environmental disturbance rejection in multi-axis optoelectronic force sensors

Gafford

Doshi‐Velez

Wood

et al. 2016

Sensors and Actuators A: Physical

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Light-Intensity Modulated (LIM) force sensors are seeing increasing interest in the field of surgical robotics and flexible systems in particular. However, such sensing modalities are notoriously susceptible to ambient effects such as temperature and environmental irradiance which can register as false force readings. We explore machine learning techniques to dynamically compensate for environmental biases that plague multi-axis optoelectronic force sensors. In this work, we fabricate a multisensor: three-axis LIM force sensor with integrated temperature and ambient irradiance sensing manufactured via a monolithic, origami-inspired fabrication process called printed-circuit MEMS. We explore machine learning regression techniques to compensate for temperature and ambient light sensitivity using on-board environmental sensor data. We compare batch-based ridge regression, kernelized regression and support vector techniques to baseline ordinary least-squares estimates to show that on-board environmental monitoring can substantially improve sensor force tracking performance and output stability under variable lighting and large (>100C) thermal gradients. By augmenting the least-squares estimate with nonlinear functions describing both environmental disturbances and cross-axis coupling effects, we can reduce the error in F x , F y and F z by 10%, 33%, and 73%, respectively. We assess viability of each algorithm tested in terms of both prediction accuracy and computational overhead, and analyze kernel-based regression for prediction in the context of online force feedback and haptics applications in surgical robotics. Finally, we suggest future work for fast approximation and prediction using stochastic, sparse kernel techniques.

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Section: Introductionmentioning

confidence: 99%

Machine learning approaches to environmental disturbance rejection in multi-axis optoelectronic force sensors

Gafford

Doshi‐Velez

Wood

et al. 2016

Sensors and Actuators A: Physical

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“…In [15], Calibration of CPS is discussed using circuits. In [16], calibration of CPS is done using least square support vector regression, and for temperature compensation one more CPS is used. In [17], extension of linearity is achieved using Hermite neural network algorithm.…”

Section: Introductionmentioning

confidence: 99%

An Evolutionary Optimized Algorithm Approach to Compensate the Non-linearity in Linear Variable Displacement Transducer Characteristics

Murugan¹,

Umayal²

2014

Journal of Electrical Engineering and Technology

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-Linearization of transducer characteristic plays a vital role in electronic instrumentation because all transducers have outputs nonlinearly related to the physical variables they sense. If the transducer output is nonlinear, it will produce a whole assortment of problems. Transducers rarely possess a perfectly linear transfer characteristic, but always have some degree of non-linearity over their range of operation. Attempts have been made by many researchers to increase the range of linearity of transducers. This paper presents a method to compensate nonlinearity of Linear Variable Displacement Transducer (LVDT) based on Extreme Learning Machine (ELM) method, Differential Evolution (DE) algorithm and Artificial Neural Network (ANN) trained by Genetic Algorithm (GA). Because of the mechanism structure, LVDT often exhibit inherent nonlinear input-output characteristics. The best approximation capability of optimized ANN technique is beneficial to this. The use of this proposed method is demonstrated through computer simulation with the experimental data of two different LVDTs. The results reveal that the proposed method compensated the presence of nonlinearity in the displacement transducer with very low training time, lowest Mean Square Error (MSE) value and better linearity. This research work involves less computational complexity and it behaves a good performance for nonlinearity compensation for LVDT and has good application prospect.

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“…Guo et al in [17] established a model based on SVR to correct the nonlinear error of photoelectric displacement sensor. Wang in [18] used SVR to make nonlinear estimation and temperature compensation of capacitor pressure sensors.…”

Section: Introductionmentioning

confidence: 99%

A Robust Static Decoupling Algorithm for 3-Axis Force Sensors Based on Coupling Error Model and ε-SVR

Song

Xiao

2012

Sensors

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Coupling errors are major threats to the accuracy of 3-axis force sensors. Design of decoupling algorithms is a challenging topic due to the uncertainty of coupling errors. The conventional nonlinear decoupling algorithms by a standard Neural Network (NN) are sometimes unstable due to overfitting. In order to avoid overfitting and minimize the negative effect of random noises and gross errors in calibration data, we propose a novel nonlinear static decoupling algorithm based on the establishment of a coupling error model. Instead of regarding the whole system as a black box in conventional algorithm, the coupling error model is designed by the principle of coupling errors, in which the nonlinear relationships between forces and coupling errors in each dimension are calculated separately. Six separate Support Vector Regressions (SVRs) are employed for their ability to perform adaptive, nonlinear data fitting. The decoupling performance of the proposed algorithm is compared with the conventional method by utilizing obtained data from the static calibration experiment of a 3-axis force sensor. Experimental results show that the proposed decoupling algorithm gives more robust performance with high efficiency and decoupling accuracy, and can thus be potentially applied to the decoupling application of 3-axis force sensors.

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Non-linearity Estimation and Temperature Compensation of Capacitor Pressure Sensors Using Least Square Support Vector Regression

Cited by 7 publications

References 8 publications

Machine learning approaches to environmental disturbance rejection in multi-axis optoelectronic force sensors

Machine learning approaches to environmental disturbance rejection in multi-axis optoelectronic force sensors

An Evolutionary Optimized Algorithm Approach to Compensate the Non-linearity in Linear Variable Displacement Transducer Characteristics

A Robust Static Decoupling Algorithm for 3-Axis Force Sensors Based on Coupling Error Model and ε-SVR

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