光学精密工程2025，Vol.33Issue(23)：3702-3713,12.DOI:10.37188/OPE.20253323.3702
融合BO-CNN-BiLSTM的压电式六维力/力矩传感器非线性解耦
Decoupling of piezoelectric six-dimensional force sensors incorporating BO-CNN-BiLSTM
摘要
Abstract
To address the degradation of force measurement performance caused by interdimensional cou-pling in piezoelectric six-dimensional force/torque sensors,an integrated decoupling algorithm(BO-CNN-BiLSTM)combining Bayesian optimization(BO),convolutional neural networks(CNN),and bidirec-tional long short-term memory networks(BiLSTM)is proposed.In this algorithm,CNN is first em-ployed to enhance the extraction of spatial coupling features from six-dimensional force signals.BiLSTM is then utilized to exploit bidirectional temporal modeling capabilities and dynamically capture cross-dimen-sional time-domain dependencies of the loads.Subsequently,BO is introduced to achieve adaptive global optimization of hyperparameters.In this way,the limitations of traditional decoupling methods in terms of real-time performance,generalization ability,and physical consistency are effectively overcome.The pro-posed BO-CNN-BiLSTM algorithm eliminates the empirical dependence on manually tuned parameters in conventional approaches and enables adaptive modeling of the nonlinear characteristics of sensors.Experi-mental results demonstrate that the maximum nonlinear error and cross-coupling error of the six-dimension-al force/torque sensor outputs are 0.87%and 0.52%,respectively.The BO-CNN-BiLSTM decoupling algorithm effectively reduces both intra-dimensional and interdimensional coupling in six-dimensional force sensors,significantly improving measurement accuracy and providing important support for anthropomor-phic motion control and environmental interaction in humanoid robots.关键词
六维力传感器/静态解耦/贝叶斯优化/卷积双向长短期记忆网络Key words
six-dimensional force sensors/static decoupling/Bayesian optimization/CNN-BiLSTM分类
信息技术与安全科学引用本文复制引用
CHU Hongbo,WANG Guicong,GAO Jialong,LI Yingjun..融合BO-CNN-BiLSTM的压电式六维力/力矩传感器非线性解耦[J].光学精密工程,2025,33(23):3702-3713,12.基金项目
山东省自然科学基金资助项目(No.ZR2023ME109) （No.ZR2023ME109）
山东省科技型中小企业创新能力提升工程资助项目(No.2024TSGC0912) （No.2024TSGC0912）
济南市"新高校20条"科研带头人工作资助项目(No.202228116) （No.202228116）
