SONG, Shengli ;ZHANG, Xinglong ;TAN, Zhitao . RBF Neural Network Based Sliding Mode Control of a Lower Limb Exoskeleton Suit. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 60, n.6, p. 437-446, june 2018. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/sl/article/rbf-neural-network-based-sliding-mode-control-of-a-lower-limb-exoskeleton-suit/>. Date accessed: 20 dec. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2013.1366.
Song, S., Zhang, X., & Tan, Z. (2014). RBF Neural Network Based Sliding Mode Control of a Lower Limb Exoskeleton Suit. Strojniški vestnik - Journal of Mechanical Engineering, 60(6), 437-446. doi:http://dx.doi.org/10.5545/sv-jme.2013.1366
@article{sv-jmesv-jme.2013.1366,
author = {Shengli Song and Xinglong Zhang and Zhitao Tan},
title = {RBF Neural Network Based Sliding Mode Control of a Lower Limb Exoskeleton Suit},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {60},
number = {6},
year = {2014},
keywords = {exoskeleton suit, hydraulic servo system, force tracking, sliding mode control, RBF neural network},
abstract = {A new force tracking control algorithm of partial actuated lower limb exoskeleton suit, which is designed for enhancing human motion is presented in this paper. Firstly, a mathematical model of the electro-hydraulic servo system was created, and equations for the frictions in the hydraulic valve and actuator were obtained. Secondly, the appropriate observer based on the estimated functions and the measurement error equations are presented for the sliding mode control algorithm. Thirdly, a sliding mode controller with applicable surface coefficient has been designed for force tracking control of the servo system. Fourthly, so as to reduce the error caused by the unchangeable surface of the sliding mode control, a radial basis functions (RBF) neural network control algorithm has been introduced to offset the disadvantage of the sliding mode control by moving the sliding surface effectively. Finally, the simulation results under conditions of different frequencies and the trial results based on the human motion of sliding mode control and the RBF based sliding control are presented, which indicate that RBF based sliding control provides a better performance than regular sliding mode control.},
issn = {0039-2480}, pages = {437-446}, doi = {10.5545/sv-jme.2013.1366},
url = {https://www.sv-jme.eu/sl/article/rbf-neural-network-based-sliding-mode-control-of-a-lower-limb-exoskeleton-suit/}
}
Song, S.,Zhang, X.,Tan, Z. 2014 June 60. RBF Neural Network Based Sliding Mode Control of a Lower Limb Exoskeleton Suit. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 60:6
%A Song, Shengli %A Zhang, Xinglong %A Tan, Zhitao %D 2014 %T RBF Neural Network Based Sliding Mode Control of a Lower Limb Exoskeleton Suit %B 2014 %9 exoskeleton suit, hydraulic servo system, force tracking, sliding mode control, RBF neural network %! RBF Neural Network Based Sliding Mode Control of a Lower Limb Exoskeleton Suit %K exoskeleton suit, hydraulic servo system, force tracking, sliding mode control, RBF neural network %X A new force tracking control algorithm of partial actuated lower limb exoskeleton suit, which is designed for enhancing human motion is presented in this paper. Firstly, a mathematical model of the electro-hydraulic servo system was created, and equations for the frictions in the hydraulic valve and actuator were obtained. Secondly, the appropriate observer based on the estimated functions and the measurement error equations are presented for the sliding mode control algorithm. Thirdly, a sliding mode controller with applicable surface coefficient has been designed for force tracking control of the servo system. Fourthly, so as to reduce the error caused by the unchangeable surface of the sliding mode control, a radial basis functions (RBF) neural network control algorithm has been introduced to offset the disadvantage of the sliding mode control by moving the sliding surface effectively. Finally, the simulation results under conditions of different frequencies and the trial results based on the human motion of sliding mode control and the RBF based sliding control are presented, which indicate that RBF based sliding control provides a better performance than regular sliding mode control. %U https://www.sv-jme.eu/sl/article/rbf-neural-network-based-sliding-mode-control-of-a-lower-limb-exoskeleton-suit/ %0 Journal Article %R 10.5545/sv-jme.2013.1366 %& 437 %P 10 %J Strojniški vestnik - Journal of Mechanical Engineering %V 60 %N 6 %@ 0039-2480 %8 2018-06-28 %7 2018-06-28
Song, Shengli, Xinglong Zhang, & Zhitao Tan. "RBF Neural Network Based Sliding Mode Control of a Lower Limb Exoskeleton Suit." Strojniški vestnik - Journal of Mechanical Engineering [Online], 60.6 (2014): 437-446. Web. 20 Dec. 2024
TY - JOUR AU - Song, Shengli AU - Zhang, Xinglong AU - Tan, Zhitao PY - 2014 TI - RBF Neural Network Based Sliding Mode Control of a Lower Limb Exoskeleton Suit JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2013.1366 KW - exoskeleton suit, hydraulic servo system, force tracking, sliding mode control, RBF neural network N2 - A new force tracking control algorithm of partial actuated lower limb exoskeleton suit, which is designed for enhancing human motion is presented in this paper. Firstly, a mathematical model of the electro-hydraulic servo system was created, and equations for the frictions in the hydraulic valve and actuator were obtained. Secondly, the appropriate observer based on the estimated functions and the measurement error equations are presented for the sliding mode control algorithm. Thirdly, a sliding mode controller with applicable surface coefficient has been designed for force tracking control of the servo system. Fourthly, so as to reduce the error caused by the unchangeable surface of the sliding mode control, a radial basis functions (RBF) neural network control algorithm has been introduced to offset the disadvantage of the sliding mode control by moving the sliding surface effectively. Finally, the simulation results under conditions of different frequencies and the trial results based on the human motion of sliding mode control and the RBF based sliding control are presented, which indicate that RBF based sliding control provides a better performance than regular sliding mode control. UR - https://www.sv-jme.eu/sl/article/rbf-neural-network-based-sliding-mode-control-of-a-lower-limb-exoskeleton-suit/
@article{{sv-jme}{sv-jme.2013.1366},
author = {Song, S., Zhang, X., Tan, Z.},
title = {RBF Neural Network Based Sliding Mode Control of a Lower Limb Exoskeleton Suit},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {60},
number = {6},
year = {2014},
doi = {10.5545/sv-jme.2013.1366},
url = {https://www.sv-jme.eu/sl/article/rbf-neural-network-based-sliding-mode-control-of-a-lower-limb-exoskeleton-suit/}
}
TY - JOUR AU - Song, Shengli AU - Zhang, Xinglong AU - Tan, Zhitao PY - 2018/06/28 TI - RBF Neural Network Based Sliding Mode Control of a Lower Limb Exoskeleton Suit JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 60, No 6 (2014): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2013.1366 KW - exoskeleton suit, hydraulic servo system, force tracking, sliding mode control, RBF neural network N2 - A new force tracking control algorithm of partial actuated lower limb exoskeleton suit, which is designed for enhancing human motion is presented in this paper. Firstly, a mathematical model of the electro-hydraulic servo system was created, and equations for the frictions in the hydraulic valve and actuator were obtained. Secondly, the appropriate observer based on the estimated functions and the measurement error equations are presented for the sliding mode control algorithm. Thirdly, a sliding mode controller with applicable surface coefficient has been designed for force tracking control of the servo system. Fourthly, so as to reduce the error caused by the unchangeable surface of the sliding mode control, a radial basis functions (RBF) neural network control algorithm has been introduced to offset the disadvantage of the sliding mode control by moving the sliding surface effectively. Finally, the simulation results under conditions of different frequencies and the trial results based on the human motion of sliding mode control and the RBF based sliding control are presented, which indicate that RBF based sliding control provides a better performance than regular sliding mode control. UR - https://www.sv-jme.eu/sl/article/rbf-neural-network-based-sliding-mode-control-of-a-lower-limb-exoskeleton-suit/
Song, Shengli, Zhang, Xinglong, AND Tan, Zhitao. "RBF Neural Network Based Sliding Mode Control of a Lower Limb Exoskeleton Suit" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 60 Number 6 (28 June 2018)
Strojniški vestnik - Journal of Mechanical Engineering 60(2014)6, 437-446
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
A new force tracking control algorithm of partial actuated lower limb exoskeleton suit, which is designed for enhancing human motion is presented in this paper. Firstly, a mathematical model of the electro-hydraulic servo system was created, and equations for the frictions in the hydraulic valve and actuator were obtained. Secondly, the appropriate observer based on the estimated functions and the measurement error equations are presented for the sliding mode control algorithm. Thirdly, a sliding mode controller with applicable surface coefficient has been designed for force tracking control of the servo system. Fourthly, so as to reduce the error caused by the unchangeable surface of the sliding mode control, a radial basis functions (RBF) neural network control algorithm has been introduced to offset the disadvantage of the sliding mode control by moving the sliding surface effectively. Finally, the simulation results under conditions of different frequencies and the trial results based on the human motion of sliding mode control and the RBF based sliding control are presented, which indicate that RBF based sliding control provides a better performance than regular sliding mode control.