DocumentCode
175481
Title
Reinforcement learning control for gas collector pressure of coke ovens
Author
Qin Bin ; Dai Leqiang ; Li Pengcheng ; Zhu Wangli ; Wang Xin
Author_Institution
Sch. of Electr. & Inf. Eng., Hunan Univ. of Technol., Zhuzhou, China
fYear
2014
fDate
May 31 2014-June 2 2014
Firstpage
414
Lastpage
417
Abstract
Due to nonlinear and strong coupling of coke ovens, a distributed reinforcement learning control algorithm based on a radial basis function (RBF) for gas collector pressure control was proposed, in which the discrete space was dealt with by RBF and the critic-actor method was used to update the parameters of search network and evaluation network of the controller. Moreover, the control strategies of the controller were optimized by trial and error in the strong interference environment. The simulation of gas collector pressure control of coke ovens has shown that the algorithm can effectively solve the coupling problem, and has better stability.
Keywords
coke; control engineering computing; fuel processing industries; learning (artificial intelligence); nonlinear control systems; ovens; pressure control; radial basis function networks; stability; RBF; coke ovens; critic-actor method; distributed reinforcement learning control; gas collector pressure control; nonlinear coupling; radial basis function; stability; strong coupling; Couplings; Educational institutions; Interference; Learning (artificial intelligence); Ovens; Pressure control; Valves; coke oven; coupling control; gas collector pressure; reinforcement learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference (2014 CCDC), The 26th Chinese
Conference_Location
Changsha
Print_ISBN
978-1-4799-3707-3
Type
conf
DOI
10.1109/CCDC.2014.6852183
Filename
6852183
Link To Document