A New Integral Critic Learning for Optimal Tracking Control with Applications to Boiler-Turbine Systems

doi:10.1002/oca.2792

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 复杂系统智能机理与平行控制团队

	A New Integral Critic Learning for Optimal Tracking Control with Applications to Boiler-Turbine Systems
	Wei, Qinglai1 ; Liu, Yujia 1; Lu, Jingwei1 ; Ling, Jun 2; Luan, Zhenhua 3; Chen, Mingliang 3
发表期刊	OPTIMAL CONTROL APPLICATIONS & METHODS
ISSN	0143-2087
	2021-10-12
页码	16
通讯作者	Wei, Qinglai(qinglai.wei@ia.ac.cn)
摘要	Optimal control theory and reinforcement learning are gradually being used in the field of industrial control. In this article, a new optimal tracking control scheme for 160 MW boiler-turbine systems is proposed based on an online policy iteration integral reinforcement learning (IRL) method. Firstly, a self-learning state tracking control with a cost function is developed to deal with the optimal tracking control problems for the boiler-turbine nonlinear system. Then with a modified cost function, a policy iteration-based IRL method is introduced to obtain the optimal control law. Finally, the monotonicity and the convergence of the cost function is analyzed and the detailed implementation of the policy iteration-based IRL method is provided via neural networks. The simulation results show that the control of the boiler-turbine system can converge in a short time by using this online iterative method. Through a theoretical simulation case, it can be concluded that the proposed method is more advanced compared with the MPC method.
关键词	adaptive dynamic programming boiler-turbine system integral reinforcement learning neural network policy iteration
DOI	10.1002/oca.2792
关键词[WOS]	NONLINEAR-SYSTEMS ; PARALLEL CONTROL ; DRUM ; UNIT
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China[2018YFB1702300] ; National Key Research and Development Program of China[2018AAA0101502] ; National Natural Science Foundation of China[62073321] ; National Natural Science Foundation of China[61873300]
项目资助者	National Key Research and Development Program of China ; National Natural Science Foundation of China
WOS研究方向	Automation & Control Systems ; Operations Research & Management Science ; Mathematics
WOS类目	Automation & Control Systems ; Operations Research & Management Science ; Mathematics, Applied
WOS记录号	WOS:000706550400001
出版者	WILEY
七大方向——子方向分类	智能控制
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/46183
专题	多模态人工智能系统全国重点实验室_复杂系统智能机理与平行控制团队中国科学院自动化研究所
通讯作者	Wei, Qinglai
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 2.Shanghai Jiao Tong Univ, Dept Automat, Shanghai, Peoples R China 3.China Nucl Power Engn CO LTD, State Key Lab Nucl Power Safety Monitoring Techno, Shenzhen, Guangdong, Peoples R China
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Wei, Qinglai,Liu, Yujia,Lu, Jingwei,et al. A New Integral Critic Learning for Optimal Tracking Control with Applications to Boiler-Turbine Systems[J]. OPTIMAL CONTROL APPLICATIONS & METHODS,2021:16.
APA	Wei, Qinglai,Liu, Yujia,Lu, Jingwei,Ling, Jun,Luan, Zhenhua,&Chen, Mingliang.(2021).A New Integral Critic Learning for Optimal Tracking Control with Applications to Boiler-Turbine Systems.OPTIMAL CONTROL APPLICATIONS & METHODS,16.
MLA	Wei, Qinglai,et al."A New Integral Critic Learning for Optimal Tracking Control with Applications to Boiler-Turbine Systems".OPTIMAL CONTROL APPLICATIONS & METHODS (2021):16.