Decoupling Algorithm of Piezoelectric Six-Dimensional Force/Torque Sensor Using Long Short-Term Memory Neural Network

QI Zhenguang; WANG Guicong; CHU Hongbo; ZHANG Shuai; LI Yingjun

doi:10.7652/xjtuxb202504015

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Decoupling Algorithm of Piezoelectric Six-Dimensional Force/Torque Sensor Using Long Short-Term Memory Neural Network

更新时间：2026-01-06

- Decoupling Algorithm of Piezoelectric Six-Dimensional Force/Torque Sensor Using Long Short-Term Memory Neural Network
- Journal of Xi’an Jiaotong University Vol. 59, Issue 4, Pages: 158-170(2025)
- 作者机构：
  
  1.济南大学机械工程学院， 250022，济南
  2.山东省传感技术与高精度衡器重点实验室， 250022，济南
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202504015
  CLC： TP212.9;TN384
- Received：08 September 2024，
  
  Online First：20 November 2024，
  
  Published：10 April 2025
- 稿件说明：
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QI Zhenguang, WANG Guicong, CHU Hongbo, et al. Decoupling Algorithm of Piezoelectric Six-Dimensional Force/Torque Sensor Using Long Short-Term Memory Neural Network[J]. Journal of Xi’an Jiaotong University, 2025, 59(4): 158-170.
DOI：

QI Zhenguang, WANG Guicong, CHU Hongbo, et al. Decoupling Algorithm of Piezoelectric Six-Dimensional Force/Torque Sensor Using Long Short-Term Memory Neural Network[J]. Journal of Xi’an Jiaotong University, 2025, 59(4): 158-170. DOI： 10.7652/xjtuxb202504015.

摘要

针对压电式六维力/力矩传感器存在的维间耦合导致传感器测力性能下降问题，提出了一种基于长短期记忆神经网络（LSTM）的压电式六维力/力矩传感器解耦算法。首先，通过六维力传感器静态标定实验，获取解耦算法所需的实验数据，并对其进行处理；然后，通过分析传感器维间耦合产生的原因及LSTM神经网络解耦原理，构建LSTM神经网络解耦模型；最后，采用基于LSTM神经网络的解耦算法，对传感器输出的多维非线性特性开展优化，解耦后得到传感器输入、输出之间的映射关系和对应的输出数据，并与径向基函数（RBF）及最小二乘（LS）解耦算法进行对比分析。研究结果表明：所使用四点支撑式压电六维力传感器的最大重复性误差为1.55%；采用基于LSTM的神经网络算法解耦后，传感器输出结果的最大非线性误差、交叉耦合误差分别为0.55%和0.28%，均小于RBF和LS算法。LSTM神经网络解耦算法能有效减少六维力/力矩传感器的维间耦合，提高传感器的测量精度，对航空航天领域的发展具有参考意义。

Abstract

To address the issue of decreased force measurement performance in piezoelectric six-dimensional force/torque sensors due to inter-dimensional coupling

a decoupling algorithm for piezoelectric six-dimensional force/torque sensors based on long short-term memory neural network (LSTM) is proposed. Firstly

experimental data needed for the decoupling algorithm is obtained through static calibration experiments of the six-dimensional force sensor and processed accordingly. Then

by analyzing the causes of inter-dimensional coupling in sensors and the decoupling principle of the LSTM neural network

an LSTM neural network decoupling model is constructed. Finally

the decoupling algorithm based on the LSTM neural network is used to optimize the multi-dimensional nonlinear characteristics of the sensor output

obtaining the mapping relationship between sensor inputs and outputs after decoupling

and corresponding output data. A comparative analysis is conducted with radial basis function (RBF) and least squares (LS) decoupling algorithms. The research results show that the maximum repeatability error of the four-point supported piezoelectric six-dimensional force sensor is 1.55%. After utilizing the LSTM-based neural network algorithm for decoupling

the maximum nonlinear error and cross-coupling error of the sensor output results are 0.55% and 0.28% respectively

both lower than those of the RBF and LS algorithms. The LSTM neural network decoupling algorithm effectively reduces inter-dimensional coupling in the six-dimensional force/torque sensor

improving measurement accuracy and holding important implications for the development of the aerospace industry.

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references

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