LIU, Lei ;HU, Jian ;WANG, Yuangang ;XIE, Zhiwei . Neural Network-Based High-Accuracy Motion Control of a Class of Torque-Controlled Motor Servo Systems with Input Saturation. Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 63, n.9, p. 519-528, june 2018. ISSN 0039-2480. Available at: <https://www.sv-jme.eu/article/neural-network-based-high-accuracy-motion-control-of-a-class-of-torque-controlled-motor-servo-systems-with-input-saturation/>. Date accessed: 20 dec. 2024. doi:http://dx.doi.org/10.5545/sv-jme.2016.4282.
Liu, L., Hu, J., Wang, Y., & Xie, Z. (2017). Neural Network-Based High-Accuracy Motion Control of a Class of Torque-Controlled Motor Servo Systems with Input Saturation. Strojniški vestnik - Journal of Mechanical Engineering, 63(9), 519-528. doi:http://dx.doi.org/10.5545/sv-jme.2016.4282
@article{sv-jmesv-jme.2016.4282,
author = {Lei Liu and Jian Hu and Yuangang Wang and Zhiwei Xie},
title = {Neural Network-Based High-Accuracy Motion Control of a Class of Torque-Controlled Motor Servo Systems with Input Saturation},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {63},
number = {9},
year = {2017},
keywords = {torque-controlled motor, input saturation, adaptive robust control, neural network, disturbance},
abstract = {The torque-controlled motor servo system is widely used in many industrial applications. However, input saturation often occurs due to the limitation of the actuator output ability, which may worsen the system control performance. In this paper, in order to inhibit the impact of input saturation on the system, a single-hidden-layer neural network based observer is designed to estimate the value of input saturation, which could later be compensated in the proposed controller. In addition, an adaptive law is introduced to estimate the unknown parameters, and a nonlinear robust term is designed to overcome the time-varying disturbances and other compensation errors. The Lyapunov theorem is used to prove the stability of the proposed controller with the neural network-based observer. Extensive comparative experimental results are obtained to verify the high-performance of the proposed control strategy.},
issn = {0039-2480}, pages = {519-528}, doi = {10.5545/sv-jme.2016.4282},
url = {https://www.sv-jme.eu/article/neural-network-based-high-accuracy-motion-control-of-a-class-of-torque-controlled-motor-servo-systems-with-input-saturation/}
}
Liu, L.,Hu, J.,Wang, Y.,Xie, Z. 2017 June 63. Neural Network-Based High-Accuracy Motion Control of a Class of Torque-Controlled Motor Servo Systems with Input Saturation. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 63:9
%A Liu, Lei %A Hu, Jian %A Wang, Yuangang %A Xie, Zhiwei %D 2017 %T Neural Network-Based High-Accuracy Motion Control of a Class of Torque-Controlled Motor Servo Systems with Input Saturation %B 2017 %9 torque-controlled motor, input saturation, adaptive robust control, neural network, disturbance %! Neural Network-Based High-Accuracy Motion Control of a Class of Torque-Controlled Motor Servo Systems with Input Saturation %K torque-controlled motor, input saturation, adaptive robust control, neural network, disturbance %X The torque-controlled motor servo system is widely used in many industrial applications. However, input saturation often occurs due to the limitation of the actuator output ability, which may worsen the system control performance. In this paper, in order to inhibit the impact of input saturation on the system, a single-hidden-layer neural network based observer is designed to estimate the value of input saturation, which could later be compensated in the proposed controller. In addition, an adaptive law is introduced to estimate the unknown parameters, and a nonlinear robust term is designed to overcome the time-varying disturbances and other compensation errors. The Lyapunov theorem is used to prove the stability of the proposed controller with the neural network-based observer. Extensive comparative experimental results are obtained to verify the high-performance of the proposed control strategy. %U https://www.sv-jme.eu/article/neural-network-based-high-accuracy-motion-control-of-a-class-of-torque-controlled-motor-servo-systems-with-input-saturation/ %0 Journal Article %R 10.5545/sv-jme.2016.4282 %& 519 %P 10 %J Strojniški vestnik - Journal of Mechanical Engineering %V 63 %N 9 %@ 0039-2480 %8 2018-06-27 %7 2018-06-27
Liu, Lei, Jian Hu, Yuangang Wang, & Zhiwei Xie. "Neural Network-Based High-Accuracy Motion Control of a Class of Torque-Controlled Motor Servo Systems with Input Saturation." Strojniški vestnik - Journal of Mechanical Engineering [Online], 63.9 (2017): 519-528. Web. 20 Dec. 2024
TY - JOUR AU - Liu, Lei AU - Hu, Jian AU - Wang, Yuangang AU - Xie, Zhiwei PY - 2017 TI - Neural Network-Based High-Accuracy Motion Control of a Class of Torque-Controlled Motor Servo Systems with Input Saturation JF - Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2016.4282 KW - torque-controlled motor, input saturation, adaptive robust control, neural network, disturbance N2 - The torque-controlled motor servo system is widely used in many industrial applications. However, input saturation often occurs due to the limitation of the actuator output ability, which may worsen the system control performance. In this paper, in order to inhibit the impact of input saturation on the system, a single-hidden-layer neural network based observer is designed to estimate the value of input saturation, which could later be compensated in the proposed controller. In addition, an adaptive law is introduced to estimate the unknown parameters, and a nonlinear robust term is designed to overcome the time-varying disturbances and other compensation errors. The Lyapunov theorem is used to prove the stability of the proposed controller with the neural network-based observer. Extensive comparative experimental results are obtained to verify the high-performance of the proposed control strategy. UR - https://www.sv-jme.eu/article/neural-network-based-high-accuracy-motion-control-of-a-class-of-torque-controlled-motor-servo-systems-with-input-saturation/
@article{{sv-jme}{sv-jme.2016.4282},
author = {Liu, L., Hu, J., Wang, Y., Xie, Z.},
title = {Neural Network-Based High-Accuracy Motion Control of a Class of Torque-Controlled Motor Servo Systems with Input Saturation},
journal = {Strojniški vestnik - Journal of Mechanical Engineering},
volume = {63},
number = {9},
year = {2017},
doi = {10.5545/sv-jme.2016.4282},
url = {https://www.sv-jme.eu/article/neural-network-based-high-accuracy-motion-control-of-a-class-of-torque-controlled-motor-servo-systems-with-input-saturation/}
}
TY - JOUR AU - Liu, Lei AU - Hu, Jian AU - Wang, Yuangang AU - Xie, Zhiwei PY - 2018/06/27 TI - Neural Network-Based High-Accuracy Motion Control of a Class of Torque-Controlled Motor Servo Systems with Input Saturation JF - Strojniški vestnik - Journal of Mechanical Engineering; Vol 63, No 9 (2017): Strojniški vestnik - Journal of Mechanical Engineering DO - 10.5545/sv-jme.2016.4282 KW - torque-controlled motor, input saturation, adaptive robust control, neural network, disturbance N2 - The torque-controlled motor servo system is widely used in many industrial applications. However, input saturation often occurs due to the limitation of the actuator output ability, which may worsen the system control performance. In this paper, in order to inhibit the impact of input saturation on the system, a single-hidden-layer neural network based observer is designed to estimate the value of input saturation, which could later be compensated in the proposed controller. In addition, an adaptive law is introduced to estimate the unknown parameters, and a nonlinear robust term is designed to overcome the time-varying disturbances and other compensation errors. The Lyapunov theorem is used to prove the stability of the proposed controller with the neural network-based observer. Extensive comparative experimental results are obtained to verify the high-performance of the proposed control strategy. UR - https://www.sv-jme.eu/article/neural-network-based-high-accuracy-motion-control-of-a-class-of-torque-controlled-motor-servo-systems-with-input-saturation/
Liu, Lei, Hu, Jian, Wang, Yuangang, AND Xie, Zhiwei. "Neural Network-Based High-Accuracy Motion Control of a Class of Torque-Controlled Motor Servo Systems with Input Saturation" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 63 Number 9 (27 June 2018)
Strojniški vestnik - Journal of Mechanical Engineering 63(2017)9, 519-528
© The Authors, CC-BY 4.0 Int. Change in copyright policy from 2022, Jan 1st.
The torque-controlled motor servo system is widely used in many industrial applications. However, input saturation often occurs due to the limitation of the actuator output ability, which may worsen the system control performance. In this paper, in order to inhibit the impact of input saturation on the system, a single-hidden-layer neural network based observer is designed to estimate the value of input saturation, which could later be compensated in the proposed controller. In addition, an adaptive law is introduced to estimate the unknown parameters, and a nonlinear robust term is designed to overcome the time-varying disturbances and other compensation errors. The Lyapunov theorem is used to prove the stability of the proposed controller with the neural network-based observer. Extensive comparative experimental results are obtained to verify the high-performance of the proposed control strategy.