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A Hybrid Feedback Linearization and Neural Network Control Algorithm Applied to a Hydraulic Actuator

[+] Author Affiliations
Fabio A. P. Borges

Universidade Federal do Rio Grande, Rio Grande, Brazil

Eduardo André Perondi, Mario R. Sobczyk

Universidade Federal do Rio Grande, Porto Alegre, Brazil

Mauro A. B. Cunha

Instituto Federal Sul-rio-grandense, Pelotas, Brazil

Paper No. FPNI2016-1562, pp. V001T01A037; 9 pages
  • 9th FPNI Ph.D. Symposium on Fluid Power
  • 9th FPNI Ph.D. Symposium on Fluid Power
  • Florianópolis, SC, Brazil, October 26–28, 2016
  • Conference Sponsors: Fluid Power Net International (FPNI), Federal University of Santa Catarina (UFSC), Brazil
  • ISBN: 978-0-7918-5047-3
  • Copyright © 2016 by ASME


This paper report a research investigation that proposes to replace the inversion set present in the traditional feedback linearization approach by an artificial neural network resulting in a hybrid composition approach with a neural network and an analytical term. The method is applied into a hydraulic actuator position system together with a friction compensation approach also built using neural networks. The control strategy used is based on a cascade methodology that consists of interpreting the hydraulic positioning system model as two interconnected subsystems: a mechanical subsystem driven by a hydraulic one. As experimental results have indicated a significant system behavior dependence on the oil temperature, its effects are also studied and the proposed method was improved by the inclusion of the oil temperature information as an input for the neural network functions. Experimental results show the effectiveness of the proposed controller and their advantages when compared with the traditional analytical schemes with feedback linearization approaches.

Copyright © 2016 by ASME



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