Nonlinearity Analysis and Parameters Optimization for an Inductive Angle Sensor

Lin, Yong; Yang, Ming; Xu, Liang; Zhuang, Xiaoqi; Dong, Zixing; Li, Shiyang

doi:10.3390/s140304111

Cited by 10 publications

(12 citation statements)

References 22 publications

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“…However, the input-output characteristics of the sensor is often nonlinear, which makes the calibration of the sensor more complicated, but does not affect the use of the systematic proportional method. For the nonlinearity problem of the sensor [25]- [28], there are several ways to solve it: approximate substitution method, calculation method, lookup table method, interpolation method, hardware circuit compensation method, etc.…”

Section: Discussionmentioning

confidence: 99%

Systematic Proportional Method for Improving the Measurement Accuracy of Passive Sensor Measurement System

et al. 2020

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The accuracy and stability of excitation source determine the measurement accuracy of passive sensor measurement system, but the implementation of high-precision excitation source (especially AC excitation source) is difficult or costly in design and process. For this purpose, this paper proposes a systematic proportional method. In this method, the DC voltage converted by the excitation signal is taken as the reference voltage of the ADC used in this system. And then the ratio of the measured signal to the excitation signal is obtained by using the transmission characteristics of the ADC, therefore the amplitude fluctuation of the excitation signal is compensated. The method can greatly suppress the measurement error caused by the amplitude fluctuation of the excitation signal and the fluctuation of the ADC reference voltage, and effectively improve the measurement accuracy of the measurement system. To verify the effectiveness of the proposed method, the Wheatstone bridge measurement circuit is designed. The experimental results show that when the amplitude of the excitation signal changes 50%, the measurement result of the measurement system using non-proportional method changes about 50%, while that of the measurement system using proportional method only changes about 1%. To further improve the measurement accuracy, the DC bias voltage compensation circuit is added, and the maximum variation error of the measurement result after compensation is only 0.3%. More importantly, the method can also be extended to other linear measurement systems with excitation source in principle, which has greater application value.

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Section: Discussionmentioning

confidence: 99%

Systematic Proportional Method for Improving the Measurement Accuracy of Passive Sensor Measurement System

et al. 2020

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“…We first implement a standard PSO algorithm due to its simplicity and effectiveness in dealing with multi-modal optimization problems. PSO algorithms were originally developed in [ 38 ] inspired by the observation of bird flocking and fish schooling, which are also related to genetic algorithms, and they are widely used in both scientific research [ 66 , 67 ] and engineering applications [ 68 , 69 ]. PSO optimizes a problem by moving solution hypotheses around in the search-space according to the current hypothesis and velocity computed to the present local and global optima.…”

Section: Proposed Methodsmentioning

confidence: 99%

Joint Infrared Target Recognition and Segmentation Using a Shape Manifold-Aware Level Set

Fan

Gong

et al. 2015

Sensors

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We propose new techniques for joint recognition, segmentation and pose estimation of infrared (IR) targets. The problem is formulated in a probabilistic level set framework where a shape constrained generative model is used to provide a multi-class and multi-view shape prior and where the shape model involves a couplet of view and identity manifolds (CVIM). A level set energy function is then iteratively optimized under the shape constraints provided by the CVIM. Since both the view and identity variables are expressed explicitly in the objective function, this approach naturally accomplishes recognition, segmentation and pose estimation as joint products of the optimization process. For realistic target chips, we solve the resulting multi-modal optimization problem by adopting a particle swarm optimization (PSO) algorithm and then improve the computational efficiency by implementing a gradient-boosted PSO (GB-PSO). Evaluation was performed using the Military Sensing Information Analysis Center (SENSIAC) ATR database, and experimental results show that both of the PSO algorithms reduce the cost of shape matching during CVIM-based shape inference. Particularly, GB-PSO outperforms other recent ATR algorithms, which require intensive shape matching, either explicitly (with pre-segmentation) or implicitly (without pre-segmentation).

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“…Attempts in improving the linearity error can be found in [23,24], where a rotary IPS has been simulated with the FE Method, and optimization algorithms, such as response surface method (RSM) and particle swarm optimization (PSO), are used for searching for the optimal geometrical parameter of the device that minimize the linearity error defined as follows:…”

Section: Novel Sensor Optimizationmentioning

confidence: 99%

“…Preliminary results in this regard can be found in [22]. Previously, in [23,24], the main focus has been the optimization of the target geometry that, however, is usually fixed in industrial applications given that the target has to fit some predefined space. An enabling technology for sensor optimization in a reasonable amount of time constitutes fast virtual prototyping of the sensor.…”

Section: Introductionmentioning

confidence: 99%

Design Optimization of PCB-Based Rotary-Inductive Position Sensors

Hoxha

Passarotto

Qama³

et al. 2022

Sensors

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This paper introduces a novel methodology to optimize the design of a ratiometric rotary inductive position sensor (IPS) fabricated in printed circuit board (PCB) technology. The optimization aims at reducing the linearity error of the sensor and amplitude mismatch between the voltages on the two receiving (RX) coils. Distinct from other optimization techniques proposed in the literature, the sensor footprint and the target geometry are considered as a non-modifiable input. This is motivated by the fact that, for sensor replacement purposes, the target has to fit a predefined space. For this reason, the original optimization technique proposed in this paper modifies the shape of the RX coils to reproduce theoretical coil voltages as much as possible. The optimized RX shape was obtained by means of a non-linear least-square solver, whereas the electromagnetic simulation of the sensor is performed with an original surface integral method, which are orders of magnitude faster than commercial software based on finite elements. Comparisons between simulations and measurements performed on different prototypes of an absolute rotary sensor show the effectiveness of the optimization tool. The optimized sensors exhibit a linearity error below 0.1% of the full scale (FS) without any signal calibration or post-processing manipulation.

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Nonlinearity Analysis and Parameters Optimization for an Inductive Angle Sensor

Cited by 10 publications

References 22 publications

Systematic Proportional Method for Improving the Measurement Accuracy of Passive Sensor Measurement System

Systematic Proportional Method for Improving the Measurement Accuracy of Passive Sensor Measurement System

Joint Infrared Target Recognition and Segmentation Using a Shape Manifold-Aware Level Set

Design Optimization of PCB-Based Rotary-Inductive Position Sensors

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