WAN, Jun ;ZHOU, Zihao ;YUN, Nuo ;ZHANG, Xiao Yong ;TANG, Jinlong ;WANG, Kehong .
Friction Compensation and External Force Estimation for Robotic Systems Using a Fuzzy Neural Network Approach.
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 72, n.1-2, p. 40-51, october 2025.
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/article/friction-compensation-and-external-force-estimation-for-robotic-systems-using-a-fuzzy-neural-network-approach/>. Date accessed: 06 apr. 2026.
doi:http://dx.doi.org/10.5545/sv-jme.2025.1489.
Wan, J., Zhou, Z., Yun, N., Zhang, X., Tang, J., & Wang, K.
(2026).
Friction Compensation and External Force Estimation for Robotic Systems Using a Fuzzy Neural Network Approach.
Strojniški vestnik - Journal of Mechanical Engineering, 72(1-2), 40-51.
doi:http://dx.doi.org/10.5545/sv-jme.2025.1489
@article{sv-jmesv-jme.2025.1489,
author = {Jun Wan and Zihao Zhou and Nuo Yun and Xiao Yong Zhang and Jinlong Tang and Kehong Wang},
title = {Friction Compensation and External Force Estimation for Robotic Systems Using a Fuzzy Neural Network Approach},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {72},
number = {1-2},
year = {2026},
keywords = {Stribeck model; fuzzy neural network; friction compensation; external force estimation; },
abstract = {To address inaccurate external force estimation caused by nonlinear friction in robotic systems, this paper proposes a friction compensation and external force estimation method based on an adaptive neuro-fuzzy inference system (ANFIS). The approach integrates Stribeck friction modeling with a Takagi–Sugeno fuzzy inference structure to identify joint friction parameters from measured data. Experimental results show that ANFIS yields lower identification errors and better generalization performance than baseline methods including fuzzy neural networks, particle swarm optimization, and least squares. The implemented feedforward compensation strategy achieves maximum torque errors of 0.263 Nm and 0.184 Nm for the two joints, lower than those obtained by the compared approaches. By incorporating the identified friction model into a generalized momentum observer with median and Butterworth filtering, the proposed method reduces the root mean square error and maximum absolute error by 18.3 % and 27.9 %, respectively, and achieves a coefficient of determination (R²) of 0.994. In collision detection tests, the method identifies impact events with reduced false alarm rates under the same experimental settings, supporting its applicability to high-precision force control in robotic applications.},
issn = {0039-2480}, pages = {40-51}, doi = {10.5545/sv-jme.2025.1489},
url = {https://www.sv-jme.eu/article/friction-compensation-and-external-force-estimation-for-robotic-systems-using-a-fuzzy-neural-network-approach/}
}
Wan, J.,Zhou, Z.,Yun, N.,Zhang, X.,Tang, J.,Wang, K.
2026 October 72. Friction Compensation and External Force Estimation for Robotic Systems Using a Fuzzy Neural Network Approach. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 72:1-2
%A Wan, Jun
%A Zhou, Zihao
%A Yun, Nuo
%A Zhang, Xiao Yong
%A Tang, Jinlong
%A Wang, Kehong
%D 2026
%T Friction Compensation and External Force Estimation for Robotic Systems Using a Fuzzy Neural Network Approach
%B 2026
%9 Stribeck model; fuzzy neural network; friction compensation; external force estimation;
%! Friction Compensation and External Force Estimation for Robotic Systems Using a Fuzzy Neural Network Approach
%K Stribeck model; fuzzy neural network; friction compensation; external force estimation;
%X To address inaccurate external force estimation caused by nonlinear friction in robotic systems, this paper proposes a friction compensation and external force estimation method based on an adaptive neuro-fuzzy inference system (ANFIS). The approach integrates Stribeck friction modeling with a Takagi–Sugeno fuzzy inference structure to identify joint friction parameters from measured data. Experimental results show that ANFIS yields lower identification errors and better generalization performance than baseline methods including fuzzy neural networks, particle swarm optimization, and least squares. The implemented feedforward compensation strategy achieves maximum torque errors of 0.263 Nm and 0.184 Nm for the two joints, lower than those obtained by the compared approaches. By incorporating the identified friction model into a generalized momentum observer with median and Butterworth filtering, the proposed method reduces the root mean square error and maximum absolute error by 18.3 % and 27.9 %, respectively, and achieves a coefficient of determination (R²) of 0.994. In collision detection tests, the method identifies impact events with reduced false alarm rates under the same experimental settings, supporting its applicability to high-precision force control in robotic applications.
%U https://www.sv-jme.eu/article/friction-compensation-and-external-force-estimation-for-robotic-systems-using-a-fuzzy-neural-network-approach/
%0 Journal Article
%R 10.5545/sv-jme.2025.1489
%& 40
%P 12
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 72
%N 1-2
%@ 0039-2480
%8 2025-10-15
%7 2025-10-15
Wan, Jun, Zihao Zhou, Nuo Yun, Xiao Yong Zhang, Jinlong Tang, & Kehong Wang.
"Friction Compensation and External Force Estimation for Robotic Systems Using a Fuzzy Neural Network Approach." Strojniški vestnik - Journal of Mechanical Engineering [Online], 72.1-2 (2026): 40-51. Web. 06 Apr. 2026
TY - JOUR
AU - Wan, Jun
AU - Zhou, Zihao
AU - Yun, Nuo
AU - Zhang, Xiao Yong
AU - Tang, Jinlong
AU - Wang, Kehong
PY - 2026
TI - Friction Compensation and External Force Estimation for Robotic Systems Using a Fuzzy Neural Network Approach
JF - Strojniški vestnik - Journal of Mechanical Engineering
DO - 10.5545/sv-jme.2025.1489
KW - Stribeck model; fuzzy neural network; friction compensation; external force estimation;
N2 - To address inaccurate external force estimation caused by nonlinear friction in robotic systems, this paper proposes a friction compensation and external force estimation method based on an adaptive neuro-fuzzy inference system (ANFIS). The approach integrates Stribeck friction modeling with a Takagi–Sugeno fuzzy inference structure to identify joint friction parameters from measured data. Experimental results show that ANFIS yields lower identification errors and better generalization performance than baseline methods including fuzzy neural networks, particle swarm optimization, and least squares. The implemented feedforward compensation strategy achieves maximum torque errors of 0.263 Nm and 0.184 Nm for the two joints, lower than those obtained by the compared approaches. By incorporating the identified friction model into a generalized momentum observer with median and Butterworth filtering, the proposed method reduces the root mean square error and maximum absolute error by 18.3 % and 27.9 %, respectively, and achieves a coefficient of determination (R²) of 0.994. In collision detection tests, the method identifies impact events with reduced false alarm rates under the same experimental settings, supporting its applicability to high-precision force control in robotic applications.
UR - https://www.sv-jme.eu/article/friction-compensation-and-external-force-estimation-for-robotic-systems-using-a-fuzzy-neural-network-approach/
@article{{sv-jme}{sv-jme.2025.1489},
author = {Wan, J., Zhou, Z., Yun, N., Zhang, X., Tang, J., Wang, K.},
title = {Friction Compensation and External Force Estimation for Robotic Systems Using a Fuzzy Neural Network Approach},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {72},
number = {1-2},
year = {2026},
doi = {10.5545/sv-jme.2025.1489},
url = {https://www.sv-jme.eu/article/friction-compensation-and-external-force-estimation-for-robotic-systems-using-a-fuzzy-neural-network-approach/}
}
TY - JOUR
AU - Wan, Jun
AU - Zhou, Zihao
AU - Yun, Nuo
AU - Zhang, Xiao Yong
AU - Tang, Jinlong
AU - Wang, Kehong
PY - 2025/10/15
TI - Friction Compensation and External Force Estimation for Robotic Systems Using a Fuzzy Neural Network Approach
JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 72, No 1-2 (2026): Strojniški vestnik - Journal of Mechanical Engineering
DO - 10.5545/sv-jme.2025.1489
KW - Stribeck model, fuzzy neural network, friction compensation, external force estimation,
N2 - To address inaccurate external force estimation caused by nonlinear friction in robotic systems, this paper proposes a friction compensation and external force estimation method based on an adaptive neuro-fuzzy inference system (ANFIS). The approach integrates Stribeck friction modeling with a Takagi–Sugeno fuzzy inference structure to identify joint friction parameters from measured data. Experimental results show that ANFIS yields lower identification errors and better generalization performance than baseline methods including fuzzy neural networks, particle swarm optimization, and least squares. The implemented feedforward compensation strategy achieves maximum torque errors of 0.263 Nm and 0.184 Nm for the two joints, lower than those obtained by the compared approaches. By incorporating the identified friction model into a generalized momentum observer with median and Butterworth filtering, the proposed method reduces the root mean square error and maximum absolute error by 18.3 % and 27.9 %, respectively, and achieves a coefficient of determination (R²) of 0.994. In collision detection tests, the method identifies impact events with reduced false alarm rates under the same experimental settings, supporting its applicability to high-precision force control in robotic applications.
UR - https://www.sv-jme.eu/article/friction-compensation-and-external-force-estimation-for-robotic-systems-using-a-fuzzy-neural-network-approach/
Wan, Jun, Zhou, Zihao, Yun, Nuo, Zhang, Xiao Yong, Tang, Jinlong, AND Wang, Kehong.
"Friction Compensation and External Force Estimation for Robotic Systems Using a Fuzzy Neural Network Approach" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 72 Number 1-2 (15 October 2025)
