Paper Info
Title
Black box modeling of PIDs implemented in PLCs without structural information: a support vector regression approach
Abstract
In this report, the parameters identification of a proportional---integral---derivative (PID) algorithm implemented in a programmable logic controller (PLC) using support vector regression (SVR) is presented. This report focuses on a black box model of the PID with additional functions and modifications provided by the manufacturers and without information on the exact structure. The process of feature selection and its impact on the training and testing abilities are emphasized. The method was tested on a real PLC (Siemens and General Electric) with the implemented PID. The results show that the SVR maps the function of the PID algorithms and the modifications introduced by the manufacturer of the PLC with high accuracy. With this approach, the simulation results can be directly used to tune the PID algorithms in the PLC. The method is sufficiently universal in that it can be applied to any PI or PID algorithm implemented in the PLC with additional functions and modifications that were previously considered to be trade secrets. This method can also be an alternative for engineers who need to tune the PID and do not have any such information on the structure and cannot use the default settings for the known structures.
Year
DOI
Venue
2015
10.1007/s00521-014-1754-2
Neural Computing and Applications
Keywords
DocType
Volume
PID,Programmable logic controller,Support vector regression
Journal
26
Issue
ISSN
Citations 
3
0941-0643
1
PageRank 
References 
Authors
0.38
10
2
Name
Order
Citations
PageRank
Robert Salat1142.23
Michal Awtoniuk210.38