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 | |
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 |
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. |
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