A Heuristic Control Algorithm for Robust Internal Model Control with Arbitrary Reference Model

2014 ◽  
pp. 67-81 ◽  
Author(s):  
M. G. Skarpetis ◽  
F. N. Koumboulis ◽  
A. S. Ntellis
Keyword(s):  
Internal Model ◽  
Control Algorithm ◽  
Reference Model ◽  
Internal Model Control ◽  
Model Control ◽  
Arbitrary Reference

Research and Application on Internal Model Control Algorithm Based on Echo State Network

2020 ◽  
Author(s):  
ZHANG Li-xia ◽  
KONG Shuo ◽  
SUN Peng-fei ◽  
LIAO Yong-wen ◽  
ZENG De-liang
Keyword(s):  
Internal Model ◽  
Control Algorithm ◽  
Internal Model Control ◽  
Echo State Network ◽  
Model Control

scholarly journals Anaerobic Digestion Process Control Using a Data-Driven Internal Model Control Method

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6746
Author(s):  
Larisa Condrachi ◽  
Ramon Vilanova ◽  
Montse Meneses ◽  
Marian Barbu
Keyword(s):  
Anaerobic Digestion ◽  
Internal Model ◽  
Control Method ◽  
Reference Model ◽  
Internal Model Control ◽  
Operating Conditions ◽  
Data Driven ◽  
Anaerobic Digestion Process ◽  
Digestion Process ◽  
Model Control

Anaerobic digestion processes offer the possibility for wastewater treatment while obtaining a benefit through the obtained biogas. This paper aims to continue the effort to adopt data-driven control methods in the case of anaerobic digestion processes. The paper proposes a data-based Internal Model Control approach applied to an anaerobic digestion process. The paper deals extensively with the issue of choosing the reference model and proposing an engineering approach to this issue. The paper also addresses the issue of verifying robust stability, a very important aspect considering the uncertainties that characterize bioprocesses in general. The approach proposed in the paper is validated through a numerical simulation using the Anaerobic Digestion Model No. 1. During the validation of the proposed control solution, the main operating conditions were analyzed, such as the setpoint tracking performance, the rejection of disturbance generated by variations in the influent concentration, and the effect of the measurement noise on the controlled variable.

Adaptive filtering and neural networks for realisation of internal model control

1993 ◽  
Vol 2 (2) ◽  
pp. 67 ◽  
Author(s):  
K.J. Hunt ◽  
D. Sbarbaro
Keyword(s):  
Neural Networks ◽  
Adaptive Filtering ◽  
Internal Model ◽  
Internal Model Control ◽  
Model Control

Internal Model Control Based on GANN for a Temperature Control Electrical Furnace

2014 ◽  
Vol 7 (5) ◽  
pp. 884
Author(s):  
Abdelkader El Kebir ◽  
H. Belhadj ◽  
A. Chaker ◽  
K. Negadi
Keyword(s):  
Temperature Control ◽  
Internal Model ◽  
Internal Model Control ◽  
Electrical Furnace ◽  
Model Control

Disturbance Rejection of Nanopositioner Using Internal Model Control

2012 ◽  
Vol 2 (4) ◽  
pp. 1-7
Author(s):  
Sheilza Aggarwal ◽  
Maneesha Garg ◽  
Akhilesh Swarup
Keyword(s):  
Disturbance Rejection ◽  
Internal Model ◽  
Internal Model Control ◽  
Model Control
Sign in / Sign up

Export Citation Format

Share Document