Improved Micropositioning of 2 DOF Stage by Using the Neural Network Compensation of Plant Nonlinearities

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ČAS, Jure ;ŠAFARIČ, Riko ;ŠKORC, Gregor .
Improved Micropositioning of 2 DOF Stage by Using the Neural Network Compensation of Plant Nonlinearities. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 56, n.10, p. 599-608, october 2017. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/sl/article/improved-micropositioning-of-2-dof-stage-by-using-the-neural-network-compensation-of-plant-nonlinearities/>. Date accessed: 19 sep. 2021. 
doi:http://dx.doi.org/.
Čas, J., Šafarič, R., & Škorc, G.
(2010).
Improved Micropositioning of 2 DOF Stage by Using the Neural Network Compensation of Plant Nonlinearities.
Strojniški vestnik - Journal of Mechanical Engineering, 56(10), 599-608.
doi:http://dx.doi.org/
@article{.,
	author = {Jure  Čas and Riko  Šafarič and Gregor  Škorc},
	title = {Improved Micropositioning of 2 DOF Stage by Using the Neural Network Compensation of Plant Nonlinearities},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {56},
	number = {10},
	year = {2010},
	keywords = {piezo actuated stage; position control; hysteresis; feedforward neural networks; },
	abstract = {This paper describes the system for micropositioning of a 2 DOF mechanism with piezoelectric actuators (PEAs) called a piezo actuated stage (PAS). The PAS is fabricated by a photo structuring process from photosensitive glass and PEAs are built-on to meet the request for its precise movement. The PAS is designed as a general 2 DOF stage. It can be used for different micropositioning or microassembling tasks according to the selected end-effector. The other components of the closed-loop control system for micropositioning of PAS are the high voltage drivers, the incremental position sensors and the control processing unit. Due to the nonlinear behaviour of the system for micropositioning, the precise position control of PAS with traditional PI controller is aggravated. Concerning the plant nonlinearities, the feedforward neural networks (NN) are used as a tool for their compensation. After the training procedure with the back-propagation (BPG) algorithm, the trained NN inverse model of plant nonlinearities is used as a feedforward part of the proposed controller. The experiment results have shown that the NN compensation improves the control performance of traditional PI controller.},
	issn = {0039-2480},	pages = {599-608},	doi = {},
	url = {https://www.sv-jme.eu/sl/article/improved-micropositioning-of-2-dof-stage-by-using-the-neural-network-compensation-of-plant-nonlinearities/}
}
Čas, J.,Šafarič, R.,Škorc, G.
2010 October 56. Improved Micropositioning of 2 DOF Stage by Using the Neural Network Compensation of Plant Nonlinearities. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 56:10
%A Čas, Jure 
%A Šafarič, Riko 
%A Škorc, Gregor 
%D 2010
%T Improved Micropositioning of 2 DOF Stage by Using the Neural Network Compensation of Plant Nonlinearities
%B 2010
%9 piezo actuated stage; position control; hysteresis; feedforward neural networks; 
%! Improved Micropositioning of 2 DOF Stage by Using the Neural Network Compensation of Plant Nonlinearities
%K piezo actuated stage; position control; hysteresis; feedforward neural networks; 
%X This paper describes the system for micropositioning of a 2 DOF mechanism with piezoelectric actuators (PEAs) called a piezo actuated stage (PAS). The PAS is fabricated by a photo structuring process from photosensitive glass and PEAs are built-on to meet the request for its precise movement. The PAS is designed as a general 2 DOF stage. It can be used for different micropositioning or microassembling tasks according to the selected end-effector. The other components of the closed-loop control system for micropositioning of PAS are the high voltage drivers, the incremental position sensors and the control processing unit. Due to the nonlinear behaviour of the system for micropositioning, the precise position control of PAS with traditional PI controller is aggravated. Concerning the plant nonlinearities, the feedforward neural networks (NN) are used as a tool for their compensation. After the training procedure with the back-propagation (BPG) algorithm, the trained NN inverse model of plant nonlinearities is used as a feedforward part of the proposed controller. The experiment results have shown that the NN compensation improves the control performance of traditional PI controller.
%U https://www.sv-jme.eu/sl/article/improved-micropositioning-of-2-dof-stage-by-using-the-neural-network-compensation-of-plant-nonlinearities/
%0 Journal Article
%R 
%& 599
%P 10
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 56
%N 10
%@ 0039-2480
%8 2017-10-24
%7 2017-10-24
Čas, Jure, Riko  Šafarič, & Gregor  Škorc.
"Improved Micropositioning of 2 DOF Stage by Using the Neural Network Compensation of Plant Nonlinearities." Strojniški vestnik - Journal of Mechanical Engineering [Online], 56.10 (2010): 599-608. Web.  19 Sep. 2021
TY  - JOUR
AU  - Čas, Jure 
AU  - Šafarič, Riko 
AU  - Škorc, Gregor 
PY  - 2010
TI  - Improved Micropositioning of 2 DOF Stage by Using the Neural Network Compensation of Plant Nonlinearities
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 
KW  - piezo actuated stage; position control; hysteresis; feedforward neural networks; 
N2  - This paper describes the system for micropositioning of a 2 DOF mechanism with piezoelectric actuators (PEAs) called a piezo actuated stage (PAS). The PAS is fabricated by a photo structuring process from photosensitive glass and PEAs are built-on to meet the request for its precise movement. The PAS is designed as a general 2 DOF stage. It can be used for different micropositioning or microassembling tasks according to the selected end-effector. The other components of the closed-loop control system for micropositioning of PAS are the high voltage drivers, the incremental position sensors and the control processing unit. Due to the nonlinear behaviour of the system for micropositioning, the precise position control of PAS with traditional PI controller is aggravated. Concerning the plant nonlinearities, the feedforward neural networks (NN) are used as a tool for their compensation. After the training procedure with the back-propagation (BPG) algorithm, the trained NN inverse model of plant nonlinearities is used as a feedforward part of the proposed controller. The experiment results have shown that the NN compensation improves the control performance of traditional PI controller.
UR  - https://www.sv-jme.eu/sl/article/improved-micropositioning-of-2-dof-stage-by-using-the-neural-network-compensation-of-plant-nonlinearities/
@article{{}{.},
	author = {Čas, J., Šafarič, R., Škorc, G.},
	title = {Improved Micropositioning of 2 DOF Stage by Using the Neural Network Compensation of Plant Nonlinearities},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {56},
	number = {10},
	year = {2010},
	doi = {},
	url = {https://www.sv-jme.eu/sl/article/improved-micropositioning-of-2-dof-stage-by-using-the-neural-network-compensation-of-plant-nonlinearities/}
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TY  - JOUR
AU  - Čas, Jure 
AU  - Šafarič, Riko 
AU  - Škorc, Gregor 
PY  - 2017/10/24
TI  - Improved Micropositioning of 2 DOF Stage by Using the Neural Network Compensation of Plant Nonlinearities
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 56, No 10 (2010): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 
KW  - piezo actuated stage, position control, hysteresis, feedforward neural networks, 
N2  - This paper describes the system for micropositioning of a 2 DOF mechanism with piezoelectric actuators (PEAs) called a piezo actuated stage (PAS). The PAS is fabricated by a photo structuring process from photosensitive glass and PEAs are built-on to meet the request for its precise movement. The PAS is designed as a general 2 DOF stage. It can be used for different micropositioning or microassembling tasks according to the selected end-effector. The other components of the closed-loop control system for micropositioning of PAS are the high voltage drivers, the incremental position sensors and the control processing unit. Due to the nonlinear behaviour of the system for micropositioning, the precise position control of PAS with traditional PI controller is aggravated. Concerning the plant nonlinearities, the feedforward neural networks (NN) are used as a tool for their compensation. After the training procedure with the back-propagation (BPG) algorithm, the trained NN inverse model of plant nonlinearities is used as a feedforward part of the proposed controller. The experiment results have shown that the NN compensation improves the control performance of traditional PI controller.
UR  - https://www.sv-jme.eu/sl/article/improved-micropositioning-of-2-dof-stage-by-using-the-neural-network-compensation-of-plant-nonlinearities/
Čas, Jure, Šafarič, Riko, AND Škorc, Gregor.
"Improved Micropositioning of 2 DOF Stage by Using the Neural Network Compensation of Plant Nonlinearities" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 56 Number 10 (24 October 2017)

Avtorji

Inštitucije

  • University of Maribor, Faculty of Electrical Engineering and Computer Science, Slovenia
  • University of Maribor, Faculty of Electrical Engineering and Computer Science, Slovenia
  • RESISTEC UPR d.o.o. & Co. k.d., Kostanjevica na Krki, Slovenia

Informacije o papirju

Strojniški vestnik - Journal of Mechanical Engineering 56(2010)10, 599-608

This paper describes the system for micropositioning of a 2 DOF mechanism with piezoelectric actuators (PEAs) called a piezo actuated stage (PAS). The PAS is fabricated by a photo structuring process from photosensitive glass and PEAs are built-on to meet the request for its precise movement. The PAS is designed as a general 2 DOF stage. It can be used for different micropositioning or microassembling tasks according to the selected end-effector. The other components of the closed-loop control system for micropositioning of PAS are the high voltage drivers, the incremental position sensors and the control processing unit. Due to the nonlinear behaviour of the system for micropositioning, the precise position control of PAS with traditional PI controller is aggravated. Concerning the plant nonlinearities, the feedforward neural networks (NN) are used as a tool for their compensation. After the training procedure with the back-propagation (BPG) algorithm, the trained NN inverse model of plant nonlinearities is used as a feedforward part of the proposed controller. The experiment results have shown that the NN compensation improves the control performance of traditional PI controller.

piezo actuated stage; position control; hysteresis; feedforward neural networks;