Sensitivity and sensitivity isotropy of an 8/4–4 parallel six-axis force sensor

Li, Chenggang; Weishan, Song; Song, Yingjie

doi:10.1108/sr-06-2018-0162

Cited by 10 publications

(2 citation statements)

References 28 publications

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“…Although the above scheme proposes a six-axis acceleration measurement scheme based on a single mass, it is difficult to accurately establish the dynamic equations of the system theoretically. Since Gaillet and Reboulet began to measure the space six-axis forces with the parallel mechanism in 1983 [ 19 ], many scholars have proposed the measurement scheme of space six-axis forces based on the parallel mechanism [ 20 , 21 , 22 , 23 , 24 ]. The six-axis force sensor based on the Stewart platform generally has the characteristics of high stiffness and load capacity through distributing the loading axially over the six legs [ 25 ].…”

Section: Introductionmentioning

confidence: 99%

Forward and Inverse Dynamics of a Six-Axis Accelerometer Based on a Parallel Mechanism

Wang

You

Yang

et al. 2021

Sensors

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The solution of the dynamic equations of the six-axis accelerometer is a prerequisite for sensor calibration, structural optimization, and practical application. However, the forward dynamic equations (FDEs) and inverse dynamic equations (IDEs) of this type of system have not been completely solved due to the strongly nonlinear coupling relationship between the inputs and outputs. This article presents a comprehensive study of the FDEs and IDEs of the six-axis accelerometer based on a parallel mechanism. Firstly, two sets of dynamic equations of the sensor are constructed based on the Newton–Euler method in the configuration space. Secondly, based on the analytical solution of the sensor branch chain length, the coordination equation between the output signals of the branch chain is constructed. The FDEs of the sensor are established by combining the coordination equations and two sets of dynamic equations. Furthermore, by introducing generalized momentum and Hamiltonian function and using Legendre transformation, the vibration differential equations (VDEs) of the sensor are derived. The VDEs and Newton–Euler equations constitute the IDEs of the system. Finally, the explicit recursive algorithm for solving the quaternion in the equation is given in the phase space. Then the IDEs are solved by substituting the quaternion into the dynamic equations in the configuration space. The predicted numerical results of the established FDEs and IDEs are verified by comparing with virtual and actual experimental data. The actual experiment shows that the relative errors of the FDEs and the IDEs constructed in this article are 2.21% and 7.65%, respectively. This research provides a new strategy for further improving the practicability of the six-axis accelerometer.

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Section: Introductionmentioning

confidence: 99%

Forward and Inverse Dynamics of a Six-Axis Accelerometer Based on a Parallel Mechanism

Wang

You

Yang

et al. 2021

Sensors

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“…In general, a three-axis accelerometer acts as the head installation of the vibration test system and is used to gather the raw vibration signal synchronously from three orthogonal axes (Xiao et al, 2017;Feng et al, 2020aFeng et al, , 2020bLi et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Sensitivity calibration of a three-axis accelerometer under different temperature conditions using the hybrid GA–PSO–BPNN algorithm

Kong

2021

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Purpose Three-axis accelerometers play a vital role in monitoring the vibrations in aircraft machinery, especially in variable flight temperature environments. The sensitivity of a three-axis accelerometer under different temperature conditions needs to be calibrated before the flight test. Hence, the authors investigated the efficiency and sensitivity calibration of three-axis accelerometers under different conditions. This paper aims to propose the novel calibration algorithm for the three-axis accelerometers or the similar accelerometers. Design/methodology/approach The authors propose a hybrid genetic algorithm–particle swarm optimisation–back-propagation neural network (GA–PSO–BPNN) algorithm. This method has high global search ability, fast convergence speed and strong non-linear fitting capability; it follows the rules of natural selection and survival of the fittest. The authors describe the experimental setup for the calibration of the three-axis accelerometer using a three-comprehensive electrodynamic vibration test box, which provides different temperatures. Furthermore, to evaluate the performance of the hybrid GA–PSO–BPNN algorithm for sensitivity calibration, the authors performed a detailed comparative experimental analysis of the BPNN, GA–BPNN, PSO–BPNN and GA–PSO–BPNN algorithms under different temperatures (−55, 0 , 25 and 70 °C). Findings It has been showed that the prediction error of three-axis accelerometer under the hybrid GA–PSO–BPNN algorithm is the least (approximately ±0.1), which proved that the proposed GA–PSO–BPNN algorithm performed well on the sensitivity calibration of the three-axis accelerometer under different temperatures conditions. Originality/value The designed GA–PSO–BPNN algorithm with high global search ability, fast convergence speed and strong non-linear fitting capability has been proposed to decrease the sensitivity calibration error of three-axis accelerometer, and the hybrid algorithm could reach the global optimal solution rapidly and accurately.

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Performance analysis of over-constrained orthogonal parallel six-axis force sensor

et al. 2022

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Sensitivity and sensitivity isotropy of an 8/4–4 parallel six-axis force sensor

Cited by 10 publications

References 28 publications

Forward and Inverse Dynamics of a Six-Axis Accelerometer Based on a Parallel Mechanism

Forward and Inverse Dynamics of a Six-Axis Accelerometer Based on a Parallel Mechanism

Sensitivity calibration of a three-axis accelerometer under different temperature conditions using the hybrid GA–PSO–BPNN algorithm

Performance analysis of over-constrained orthogonal parallel six-axis force sensor

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