Title :
Modeling of Plant Dynamics and Control based on Reinforcement learning
Author :
Maeda, Tomoyuki ; Nakayama, Makishi ; Kitamura, Aya
Author_Institution :
Production Syst. Res. Lab., Kobe Steel Ltd.
Abstract :
The dynamics modeling of a plant was developed by using Q-learning, which is one method of reinforcement learning. We thought the modeling of the dynamical system to be the function approximation problem for the system output response signal, and enhanced reinforcement learning to the modeling method of the dynamical system. We describe that this modeling method guarantee to offer highly accurate dynamics models by numerical samples, which deals with incinerator´s combustion. Results of numerical simulation show that the predictive control method using these models has robust tracking property
Keywords :
learning (artificial intelligence); nonlinear dynamical systems; predictive control; process control; plant dynamics model; predictive control; reinforcement learning; Combustion; Error correction; Function approximation; Learning; Numerical models; Predictive control; Predictive models; Process control; Temperature control; Uncertainty; dynamical systems; modeling; predictive control; reinforcement learing;
Conference_Titel :
SICE-ICASE, 2006. International Joint Conference
Conference_Location :
Busan
Print_ISBN :
89-950038-4-7
Electronic_ISBN :
89-950038-5-5
DOI :
10.1109/SICE.2006.315850