Data-based Suboptimal Neuro-control Design with Reinforcement Learning for Dissipative Spatially Distributed Processes
Luo, Biao1,2; Wu, Huai-Ning1; Li, Han-Xiong3,4
发表期刊INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
2014-05-14
卷号53期号:19页码:8106-8119
文章类型Article
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.
关键词Data-based
WOS标题词Science & Technology ; Technology
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研究方向Engineering
WOS类目Engineering, Chemical
WOS记录号WOS:000336078500034
引用统计
被引频次:28[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/173211/3851
专题复杂系统管理与控制国家重点实验室_复杂系统智能机理与平行控制团队
通讯作者Wu, Huai-Ning
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
第一作者单位中国科学院自动化研究所
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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.
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