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
fDate :
May 31 2014-June 2 2014
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;
Conference_Titel :
Control and Decision Conference (2014 CCDC), The 26th Chinese
Conference_Location :
Changsha
Print_ISBN :
978-1-4799-3707-3
DOI :
10.1109/CCDC.2014.6852183