Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Robust evolving cloud-based PID control adjusted by gradient learning method
AU - Skrjanc, Igor
AU - Blazic, Sasho
AU - Angelov, Plamen
PY - 2014/6/2
Y1 - 2014/6/2
N2 - In this paper an improved robust evolving cloudbased controller (RECCo) for a class of nonlinear processes is introduced. The controller is based on parameter-free premise (IF) part. The consequence in this case is given in the form of PIDtype controller. The three adjustable parameters of PID controllerare updated on-line with a stable adaptation mechanism based on Lyapunov approach such that the output of the process tracks the desired model-reference trajectory. The proposed algorithm has also ability to add new rules or new clouds when this is necessary to improve the whole behaviour of the controlled process. This means that RECCo controller evolves the control structure andadjusts at the same time the parameters of the controller in an on-line manner, while performing the control of the plant.This approach is an example of almost parameter-free approach, because it does not use any off-line pre-training nor the explicit model of the plant and requires almost no parameter tuning. The proposed algorithm is tested on an artificial nonlinear first-order process and on a simulated hydraulic plant.
AB - In this paper an improved robust evolving cloudbased controller (RECCo) for a class of nonlinear processes is introduced. The controller is based on parameter-free premise (IF) part. The consequence in this case is given in the form of PIDtype controller. The three adjustable parameters of PID controllerare updated on-line with a stable adaptation mechanism based on Lyapunov approach such that the output of the process tracks the desired model-reference trajectory. The proposed algorithm has also ability to add new rules or new clouds when this is necessary to improve the whole behaviour of the controlled process. This means that RECCo controller evolves the control structure andadjusts at the same time the parameters of the controller in an on-line manner, while performing the control of the plant.This approach is an example of almost parameter-free approach, because it does not use any off-line pre-training nor the explicit model of the plant and requires almost no parameter tuning. The proposed algorithm is tested on an artificial nonlinear first-order process and on a simulated hydraulic plant.
U2 - 10.1109/EAIS.2014.6867480
DO - 10.1109/EAIS.2014.6867480
M3 - Conference contribution/Paper
SN - 9781479933471
SP - 1
EP - 8
BT - Proceedings 2014 IEEE Symposium on Evolving and Intelligent Systems, EAIS-2014
PB - IEEE Xplore
T2 - 2014
Y2 - 2 June 2014 through 4 June 2014
ER -