Title :
Hybrid neural network/conventional control of a benchmark process control problem
Author :
Doerschuk, Peggy Isreal ; Sarrafian, Eric ; Mekic, Mahdi ; Doerschuk, David Oakes
Author_Institution :
Lamar Univ., Beaumont, TX, USA
fDate :
6/24/1905 12:00:00 AM
Abstract :
We combine neural network and conventional controllers to form a hybrid that achieves very accurate control in both stable an unstable operating regions of a simulated bioreactor. The neural network handles nonlinearity and generalizes to cover both regions. The conventional controller eliminates the offset error incurred by generalization
Keywords :
biotechnology; generalisation (artificial intelligence); neurocontrollers; nonlinear control systems; process control; stability; benchmark process control problem; control nonlinearity; generalization; hybrid neural network/conventional control; offset error elimination; simulated bioreactor; Bioreactors; Computational modeling; Error correction; Linear feedback control systems; Neural networks; Pi control; Process control; Proportional control; Testing; Three-term control;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1005450