- title
- Multidimensional flow mapping for proportional valves
- Please use the following URL when quoting:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-710931
- conference
- 12th International Fluid Power Conference (12. IFK). Dresden, October 12 – 14, 2020
- Source
- Volume 1 – Symposium - 1
Erscheinungsort: Dresden
Verlag: Technische Universität Dresden
Erscheinungsjahr: 2020
Bandnummer Schriftenreihe: 1
Seiten: 231-240
DOI: 10.25368/2020.6 - doi
- https://doi.org/10.25368/2020.31
- Abstract (EN)
- Inverse, multidimensional input-output flow mapping is very important for use of valves in precision motion control applications. Due to the highly nonlinear characteristic and uncertain model structure of the cartridge valves, it is hard to formulate the modelling of their flow mappings into simple parameter estimation problems. This contribution conducts a comprehensive analysis and validation of three- and four-dimensional input-output-mapping approaches for a proportional pilot operated seat valves. Therefore, a virtual and a physical test-rig setup are utilized for initial measurement, implementation and assessment. After modeling and validating the valve under consideration, as a function of flow, pressure and temperature different mapping methods are investigated. More specifically, state of the art approaches, deep-learning methods and a newly developed approach (extPoly) are examined. Especially ANNs and Polynomials show reasonable approximation results even for more than two inputs. However, the results are strongly dependent on the structure and distribution of the input data points. Besides identification effort, the invertibility was investigated.
- Keywords (DE)
- 12. IFK, Ventilsteuerung, Flussabbildung, polynomische Anpassung, künstliche neuronale Netze
- Keywords (EN)
- 12th International Fluid Power Conference, Valve Control, Flow Mapping, Polynomial Fitting, Artificial Neural Networks, Deep Learning
- Classification (DDC)
- 620
- Classification (RVK)
- ZQ 5460
- university_publisher
- Technische Universität Dresden, Dresden
- corporation_other
- Dresdner Verein zur Förderung der Fluidtechnik e. V. Dresden, Dresden
- version
- publizierte Version / Verlagsversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-710931
- Qucosa date of publication
- 25.06.2020
- Document type
- in_proceeding
- Document language
- English
- licence
