控制理论与应用2016，Vol.33Issue(12)：1584-1592,9.DOI:10.7641/CTA.2016.60455
数据驱动的工业过程运行优化控制
Data-driven operational optimization control of industrial processes
摘要
Abstract
It is difficult to accurately model productive processes and describe relationship between operational indices and controlled variables for modem industrial processes.How to design the setpoints by using only data generated by operational processes for optimizing operational indices,without requiring the knowledge of model parameters of operational processes,poses a challenge on operational optimization control.This paper focuses on a class of industrial processes that can be linearized near the steady states and take different time scales adopted in the operational control loop and process control loop into account.In this context,a Q-learning based suboptimal setpoint learning algorithm is proposed to learn suboptimal setpoints by utilizing only data,such that the operational indices can track the desired values in an suboptimal manner.A simulation experiment in flotation process is implemented to show the effectiveness of the proposed method.关键词
运行优化控制/设定值/近似动态规划/Q-学习Key words
operational optimization control/setpoints/approximate dynamical programming/Q-learning分类
信息技术与安全科学引用本文复制引用
李金娜,高溪泽,柴天佑,范家璐..数据驱动的工业过程运行优化控制[J].控制理论与应用,2016,33(12):1584-1592,9.基金项目
国家自然科学基金项目(61673280,61104093,61525302,61333012,61304028,61590922,61503257),流程工业综合自动化国家重点实验室开放课题(PAL-N201603).辽宁省高等学校杰出青年学者成长计划(LJQ2015088),辽宁省自然科学基金项目(2015020164,2014020138)资助.Supported by National Natural Science Foundation of China (61673280,61104093,61525302,61333012,61304028,61590922,61503257),Open Project of State Key Laboratory of Synthetical Automation for Process Industries (PAL-N201603),Program of Distinguished Scholars Growing of Liaoning Provincial Universities (LJQ2015088) and Natural Science Foundation of Liaoning Province (2015020164,2014020138). （61673280,61104093,61525302,61333012,61304028,61590922,61503257）
