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Optimal operational control for industrial processes based o...

Optimal operational control for industrial processes based on Q-learning method

作     者:Jinna Li Xize Gao Decheng Yuan Jialu Fan 

作者单位:Lab of Operational Research and CyberneticsCollege of Information and EngineeringShenyang University of Chemical Technology State Key Lab of Synthetical Automation for Process IndustriesNortheastern University 

会议名称:《第36届中国控制会议》

主办单位:Dalian University of Technology;Systems Engineering Society of China (SESC);Technical Committee on Control Theory (TCCT), Chinese Association of Automation (CAA)

会议日期:2017年

学科分类:12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术(可授工学、理学学位)] 

基  金:supported by National Natural Science Foundation(NNSF) of China under Grant 61673280,61104093,61525302,61333012,61304028,61590922,61503257 State Key Lab of Synthetical Automation for Process Industries Open topic(PAL-N201603) Institutions of Higher Learning in Liaoning Provincial Outstanding Young Scholars Growth Plans(LJQ2015088) Liaoning Provincial Natural Science Foundation of China(2015020164,2014020138) 

关 键 词:optimal operational control setpoints approximate dynamical programming Q-learning 

摘      要:It is difficult to accurately model productive processes and describe relationship between operational indices and controlled variables for complex modern industrial *** to design the optimal setpoints by using only data generated by operational processes,without requiring the knowledge of model parameters of operational processes,poses a challenge on designing optimal *** paper presents a state-observer based Q-learning algorithm to learn the optimal setpoints by utilizing only data,such that the real operational indices can track the desired values in an approximately optimal manner.A simulation experiment in flotation process is implemented to show the effectiveness of the proposed method.

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