Temperature compensation for six-dimension force/torque sensor based on Radial Basis Function Neural Network

Sun, Yongjun; Liu, Yiwei; Liu, Hong

doi:10.1109/icinfa.2014.6932759

Cited by 7 publications

(6 citation statements)

References 13 publications

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“…) > 0 is a diagonal gain matrix. Substituting ( 16) into ( 17) and then integrating (17), the observer r(t) with initial condition r(0) � 0 can be expressed as follows:…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…In this study, the calibration matrix and temperature drift coefficient of the sensor are estimated simultaneously using linear regression based on the least square method [16]. Sun modeled the temperature drift of a six-axis force torque sensor of a space manipulator by using the least square support vector machine and particle swarm optimization algorithm to optimize the parameters of the model [17]. However, the software compensation depends on the accuracy of the model.…”

Section: Introductionmentioning

confidence: 99%

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Disturbance Elimination for the Modular Joint Torque Sensor of a Collaborative Robot

Zhang

Qin

Gao

et al. 2020

Mathematical Problems in Engineering

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The ability to obtain accurate torque information and detect collision information is the key demand for collaborative robots. Given the highly integrated modular joint, the joint torque-sensing system is disturbed by other elements during online measurement, affecting the accuracy of measurement. For example, the torque ripple caused by the periodic deformation of the flexspline of the harmonic driver and the temperature drift due to the heating of motor are the two main factors affecting the measurement of the torque sensor. In this study, a torque sensor suitable for a modular joint is developed. Moreover, a strain gauge configuration based on a specified phase difference for torque ripple cancelation and a temperature compensation method based on the combination of the Wheatstone bridge and a nonlinear model are proposed. Finally, the performance of the developed sensor is experimentally evaluated.

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“…) > 0 is a diagonal gain matrix. Substituting ( 16) into ( 17) and then integrating (17), the observer r(t) with initial condition r(0) � 0 can be expressed as follows:…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Disturbance Elimination for the Modular Joint Torque Sensor of a Collaborative Robot

Zhang

Qin

Gao

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…The artificial intelligence approaches involve BP neural networks [9]- [11] and support vector machine [12]- [14]. The empirical risk minimum (ERM) principle and gradient descent iteration are the cornerstones of BP neural networks, which may lead the modeling process fall into some pitfalls as the curse of dimensionality, local minimum, under-fitting or over-fitting, etc., [15]- [17]. Vapnik developed the support vector machine (SVM) which rooted in structural risk minimum (SRM) can obtain the global optimal solution by solving a convex optimization problem [18].…”

Section: Introductionmentioning

confidence: 99%

Temperature Compensation of Piezo-Resistive Pressure Sensor Utilizing Ensemble AMPSO-SVR Based on Improved Adaboost.RT

Zhang

et al. 2020

IEEE Access

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As the silicon material is severely influenced by the ambient temperature, the silicon piezo-resistive pressure sensor remarkably suffers from a strong nonlinearity in the response characteristic as the ambient temperature varies. To address this crucial issue, an adaptive mutation particle swarm optimization optimized support vector regression (AMPSO-SVR) combined with improved AdaBoost.RT algorithm is presented. The opposition-based learning initialization and Levy mutation is applied in the adaptive mutation particle swarm optimization (AMPSO) to achieve the appropriate model selection task which directly determines the performance of SVR. The performance of original AdaBoost.RT is improved by a dynamical modification approach for threshold and quoted error criterion. In order to verify the effectiveness of the proposed temperature compensation approach, several additional optimization methods such as Cuckoo search (CS), dragonfly algorithm (DA), multi-verse optimizer (MVO), conventional particle swarm optimization (PSO), Levy flight improved particle swarm optimization (Levy-PSO) and the AMPSO combined with SVR are investigated. The minimum quoted error, maximum quoted error, the mean quoted error and the variance of the quoted error over testing data obtained by the proposed method are 6.8764×10 −5 , 6.4463×10 −4 , 3.2619×10 −4 and 2.5714×10 −8 respectively, which are superior to the corresponding indices obtained by other methods. The analysis of simulation results indicates the method proposed in this research is applicable, effective and efficient for industrial application.

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“…It is based on work presented at the 18th International Conference CLAWAR [ 2 ]. Despite decades of reported studies on various strain gauges-based sensors for measuring joint torque over the years [ 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 ], their basic design has not improved significantly and most have restricted applicability in view of the strict requirements regarding linearity and symmetrical behavior to cope with different scales [ 20 ] necessitated by specific applications. The reason for this issue is well reported in literature, which clearly documented a trade-off between two factors i.e., sensitivity and torsional stiffness.…”

Section: Introductionmentioning

confidence: 99%

Towards Scalable Strain Gauge-Based Joint Torque Sensors

Khan

D’Imperio

Cannella

et al. 2017

Sensors

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During recent decades, strain gauge-based joint torque sensors have been commonly used to provide high-fidelity torque measurements in robotics. Although measurement of joint torque/force is often required in engineering research and development, the gluing and wiring of strain gauges used as torque sensors pose difficulties during integration within the restricted space available in small joints. The problem is compounded by the need for a scalable geometric design to measure joint torque. In this communication, we describe a novel design of a strain gauge-based mono-axial torque sensor referred to as square-cut torque sensor (SCTS), the significant features of which are high degree of linearity, symmetry, and high scalability in terms of both size and measuring range. Most importantly, SCTS provides easy access for gluing and wiring of the strain gauges on sensor surface despite the limited available space. We demonstrated that the SCTS was better in terms of symmetry (clockwise and counterclockwise rotation) and more linear. These capabilities have been shown through finite element modeling (ANSYS) confirmed by observed data obtained by load testing experiments. The high performance of SCTS was confirmed by studies involving changes in size, material and/or wings width and thickness. Finally, we demonstrated that the SCTS can be successfully implementation inside the hip joints of miniaturized hydraulically actuated quadruped robot-MiniHyQ. This communication is based on work presented at the 18th International Conference on Climbing and Walking Robots (CLAWAR).

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Temperature compensation for six-dimension force/torque sensor based on Radial Basis Function Neural Network

Cited by 7 publications

References 13 publications

Disturbance Elimination for the Modular Joint Torque Sensor of a Collaborative Robot

Disturbance Elimination for the Modular Joint Torque Sensor of a Collaborative Robot

Temperature Compensation of Piezo-Resistive Pressure Sensor Utilizing Ensemble AMPSO-SVR Based on Improved Adaboost.RT

Towards Scalable Strain Gauge-Based Joint Torque Sensors

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