Please use this identifier to cite or link to this item:
|Title:||Self-learning neurofuzzy control of a liquid helium cryostat|
|Authors:||Tan, W.W. |
|Keywords:||Adaptive neurofuzzy control on-line training|
|Citation:||Tan, W.W., Dexter, A.L. (1999-10). Self-learning neurofuzzy control of a liquid helium cryostat. Control Engineering Practice 7 (10) : 1209-1220. ScholarBank@NUS Repository. https://doi.org/10.1016/S0967-0661(99)00096-9|
|Abstract:||The paper demonstrates that a self-learning neurofuzzy controller is able to regulate the temperature in a liquid helium cryostat. In order to simplify the task of commissioning the controller, a strategy for choosing the user-selected parameters from an equivalent proportional-plus-integral controller (PI) is derived. Experimental results which illustrate the potential of the proposed control scheme are presented. The performance of the self-learning neurofuzzy controller is also compared with that of a commercial gain-scheduled PI controller.|
|Source Title:||Control Engineering Practice|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Aug 15, 2018
WEB OF SCIENCETM
checked on Jul 23, 2018
checked on May 5, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.