The use of pneumatic devices is widespread among different industrial fields, in tasks like handling or assembly. Pneumatic systems are low-cost, reliable, and compact solutions. However, its use is typically restricted to simple tasks due to the poor performance achieved in applications where accurate motion control is required. One of the key elements required to achieve a good control performance is the model of the servopneumatic system. An accurate model may be of vital importance not only in the simulation steps needed to test the control strategy but also as a part of the controller itself. This work presents a new servopneumatic system model primarily developed for control tasks, namely, to predict pneumatic and friction forces in dynamic tests. The model can also be used in simulation tasks to predict the piston position and velocity. The performance on both applications is validated experimentally.
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March 2012
Technical Briefs
A Neural Network Based Nonlinear Model of a Servopneumatic System
J. Falcão Carneiro,
e-mail: jpbrfc@fe.up.pt
J. Falcão Carneiro
IDMEC, Faculdade de Engenharia, Universidade do Porto
, Rua Dr Roberto Frias s/n, 4200-465 Porto, Portugal
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F. Gomes de Almeida
F. Gomes de Almeida
IDMEC, Faculdade de Engenharia, Universidade do Porto
, Rua Dr Roberto Frias s/n, 4200-465 Porto, Portugal
Search for other works by this author on:
J. Falcão Carneiro
IDMEC, Faculdade de Engenharia, Universidade do Porto
, Rua Dr Roberto Frias s/n, 4200-465 Porto, Portugal
e-mail: jpbrfc@fe.up.pt
F. Gomes de Almeida
IDMEC, Faculdade de Engenharia, Universidade do Porto
, Rua Dr Roberto Frias s/n, 4200-465 Porto, Portugal
J. Dyn. Sys., Meas., Control. Mar 2012, 134(2): 024502 (8 pages)
Published Online: December 30, 2011
Article history
Received:
January 21, 2011
Revised:
September 19, 2011
Online:
December 30, 2011
Published:
December 30, 2011
Citation
Falcão Carneiro, J., and Gomes de Almeida, F. (December 30, 2011). "A Neural Network Based Nonlinear Model of a Servopneumatic System." ASME. J. Dyn. Sys., Meas., Control. March 2012; 134(2): 024502. https://doi.org/10.1115/1.4005360
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