Data-based Suboptimal Neuro-control Design with Reinforcement Learning for Dissipative Spatially Distributed Processes

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 智能化团队

	Data-based Suboptimal Neuro-control Design with Reinforcement Learning for Dissipative Spatially Distributed Processes
	Luo, Biao1,2 ; Wu, Huai-Ning 1; Li, Han-Xiong 3,4
刊名	INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
	2014-05-14
卷号	53 期号:19 页码:8106-8119
英文摘要	For many real complicated industrial processes, the accurate system model is often unavailable. In this paper, we consider the partially unknown spatially distributed processes (SDPs) which are described by general highly dissipative nonlinear partial differential equations (PDEs) and develop a data-based adaptive suboptimal neuro-control method by introducing the thought of reinforcement learning (RL). First, based on the empirical eigenfunctions computed with Karhunen-Loeve decomposition, singular perturbation theory is used to derive a reduced-order model of an ordinary differential equation that represents the dominant dynamics of the SDP. Second, the Hamilton-Jacobi-Bellman (HJB) approach is used for the suboptimal control design, and the thought of policy iteration (PI) is introduced for online learning of the solution of the HJB equation, and its convergence is established. Third, a neural network (NN) is employed to approximate the cost function in the PI procedure, and a NN weight tuning algorithm based on the gradient descent method is proposed. We prove that the developed online adaptive suboptimal neuro-controller can ensure that the original closed-loop PDE system is semiglobally uniformly ultimately bounded. Finally, the developed data-based control method is applied to a nonlinear diffusion-reaction process, and the achieved results demonstrate its effectiveness.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Engineering, Chemical
研究领域[WOS]	Engineering
关键词[WOS]	OUTPUT-FEEDBACK CONTROL ; PARABOLIC PDE SYSTEMS ; DISCRETE-TIME-SYSTEMS ; WASTE-WATER TREATMENT ; PREDICTIVE CONTROL ; APPROXIMATION ; CONVERGENCE ; CONSTRAINTS ; REDUCTION ; ALGORITHM
收录类别	SCI
语种	英语
WOS记录号	WOS:000336078500034
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/3851]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_智能化团队
作者单位	1.Beijing Univ Aeronaut & Astronaut, Beihang Univ, Sch Automat Sci & Elect Engn, Sci & Technol Aircraft Control Lab, Beijing 100191, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 3.City Univ Hong Kong, Dept Syst Engn & Engn Management, Kowloon, Hong Kong, Peoples R China 4.Cent S Univ, State Key Lab High Performance Complex Mfg, Changsha, Hunan, Peoples R China
推荐引用方式 GB/T 7714	Luo, Biao,Wu, Huai-Ning,Li, Han-Xiong. Data-based Suboptimal Neuro-control Design with Reinforcement Learning for Dissipative Spatially Distributed Processes[J]. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH,2014,53(19):8106-8119.
APA	Luo, Biao,Wu, Huai-Ning,&Li, Han-Xiong.(2014).Data-based Suboptimal Neuro-control Design with Reinforcement Learning for Dissipative Spatially Distributed Processes.INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH,53(19),8106-8119.
MLA	Luo, Biao,et al."Data-based Suboptimal Neuro-control Design with Reinforcement Learning for Dissipative Spatially Distributed Processes".INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 53.19(2014):8106-8119.