Novel decoupling algorithm based on parallel voltage extreme learning machine (PV-ELM) for six-axis F/M sensors

•A novel decoupling algorithm PV-ELM for improving the performance of the six-axis robotic F/M sensors was proposed.•The effectiveness of the proposed decoupling method was proved by comparing it with traditional methods such as Least-Squares (LS), Support Vector Regression (SVR), BP Neural Network...

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Bibliographic Details
Published in:Robotics and computer-integrated manufacturing Vol. 57; pp. 303 - 314
Main Authors: Liang, Qiaokang, Long, Jianyong, Coppola, Gianmarc, Zhang, Dan, Sun, Wei
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.06.2019
Elsevier BV
Subjects:
Algorithms
Artificial neural networks
Back propagation networks
Calibration and decoupling
Coupling
Decoupling method
Electric potential
Extreme learning machine
Machine learning
Multi-axis force/moment sensors
Multiaxis
Neural networks
Robotic sensory system
Sensors
Support vector machines
Extreme learning machine
Calibration and decoupling
Multi-axis force/moment sensors
Robotic sensory system
ISSN:0736-5845, 1879-2537
Online Access:Get full text
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Summary:•A novel decoupling algorithm PV-ELM for improving the performance of the six-axis robotic F/M sensors was proposed.•The effectiveness of the proposed decoupling method was proved by comparing it with traditional methods such as Least-Squares (LS), Support Vector Regression (SVR), BP Neural Network (BPNN), and Extreme Learning Machine (ELM) methods.•The theoretical and experimental demonstrations provide a comprehensive description of the calibration and decoupling procedures of multi-axis robotic F/M sensors.•To the best of our knowledge, this report describes the first time that PV-ELM algorithm has been applied to the decoupling of six-axis F/M sensors. Accurate, time-effective calibration and decoupling procedures of multi-axis Force/Moment (F/M) sensors are critical to the implementation of these sensors. Recently, many decoupling methods have been proposed by researchers, but the inherent coupling relationship among components of multi-axis F/M sensors has not been taken into account. In this paper, we thus proposed a novel Sparse Voltage Maximum Inter-class Variance (SVMIV) algorithm, which took advantage of the inherent relationship nature. Furthermore, a novel nonlinear decoupling method based on the Parallel Voltage-Extreme Learning Machine (PV-ELM) was also presented. To demonstrate the utility of the proposed approach, extensive comparisons were made between the proposed PV-ELM decoupling method and several conventional decoupling approaches such as Least Square (LS), Support Vector Regression (SVR), Back Propagation Neural Network (BPNN), and Extreme Learning Machine (ELM). Results of real decoupling experiments demonstrated that the proposed the PV-ELM decoupling algorithm outperforms linear decoupling algorithms for six-axis F/M sensors. In addition, it was also proved that the PV-ELM decoupling method can decouple the outputs of six-axis F/M sensors with higher precision, faster speed, improved robustness, and faster convergence than the state-of-the-art nonlinear decoupling algorithms such as SVR, BP and ELM. Overall, this paper proposed a novel way to deal with the inherent coupling relationship of six-axis F/M sensors, and the experimental results demonstrated that the maximum I-type and II-type errors were below 0.356% and 0.270%, respectively, of full scale in all measured variables.
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ISSN:0736-5845
1879-2537
DOI:10.1016/j.rcim.2018.12.014