Knowledge Commons of Institute of Automation，CAS
| Adaptive Optimal Control of Highly Dissipative Nonlinear Spatially Distributed Processes With Neuro-Dynamic Programming | |
Luo, Biao1,2 ; Wu, Huai-Ning1; Li, Han-Xiong3
| |
| 发表期刊 | IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
 |
| 2015-04-01 | |
| 卷号 | 26期号:4页码:684-696 |
| 文章类型 | Article |
| 摘要 | Highly dissipative nonlinear partial differential equations (PDEs) are widely employed to describe the system dynamics of industrial spatially distributed processes (SDPs). In this paper, we consider the optimal control problem of the general highly dissipative SDPs, and propose an adaptive optimal control approach based on neuro-dynamic programming (NDP). Initially, Karhunen-Loeve decomposition is employed to compute empirical eigenfunctions (EEFs) of the SDP based on the method of snapshots. These EEFs together with singular perturbation technique are then used to obtain a finite-dimensional slow subsystem of ordinary differential equations that accurately describes the dominant dynamics of the PDE system. Subsequently, the optimal control problem is reformulated on the basis of the slow subsystem, which is further converted to solve a Hamilton-Jacobi-Bellman (HJB) equation. HJB equation is a nonlinear PDE that has proven to be impossible to solve analytically. Thus, an adaptive optimal control method is developed via NDP that solves the HJB equation online using neural network (NN) for approximating the value function; and an online NN weight tuning law is proposed without requiring an initial stabilizing control policy. Moreover, by involving the NN estimation error, we prove that the original closed-loop PDE system with the adaptive optimal control policy is semiglobally uniformly ultimately bounded. Finally, the developed method is tested on a nonlinear diffusion-convection-reaction process and applied to a temperature cooling fin of high-speed aerospace vehicle, and the achieved results show its effectiveness. |
| 关键词 | Adaptive Optimal Control Empirical Eigenfunction (Eef) Highly Dissipative Partial Differential Equations (Pdes) Neuro-dynamic Programming (Ndp) Spatially Distributed Processes (Sdps) |
| WOS标题词 | Science & Technology ; Technology |
| 关键词[WOS] | DISCRETE-TIME-SYSTEMS ; HYPERBOLIC PDE SYSTEMS ; FINITE-DIMENSIONAL APPROXIMATION ; OPTIMAL TRACKING CONTROL ; OUTPUT-FEEDBACK CONTROL ; PARAMETER-SYSTEMS ; MODEL-REDUCTION ; ROBUST-CONTROL ; ITERATION ALGORITHM ; STABILIZATION |
| 收录类别 | SCI |
| 语种 | 英语 |
| WOS研究方向 | Computer Science ; Engineering |
| WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic |
| WOS记录号 | WOS:000351835900004 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/10738 |
| 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 |
| 通讯作者 | Luo, Biao |
| 作者单位 | 1.Beihang Univ, 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, Hong Kong, Hong Kong, 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 | Luo, Biao,Wu, Huai-Ning,Li, Han-Xiong. Adaptive Optimal Control of Highly Dissipative Nonlinear Spatially Distributed Processes With Neuro-Dynamic Programming[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2015,26(4):684-696. |
| APA | Luo, Biao,Wu, Huai-Ning,&Li, Han-Xiong.(2015).Adaptive Optimal Control of Highly Dissipative Nonlinear Spatially Distributed Processes With Neuro-Dynamic Programming.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,26(4),684-696. |
| MLA | Luo, Biao,et al."Adaptive Optimal Control of Highly Dissipative Nonlinear Spatially Distributed Processes With Neuro-Dynamic Programming".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 26.4(2015):684-696. |
| 条目包含的文件 | 下载所有文件 | |||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| 2015IEEE TNNLS Adapt（2465KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 | |
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
修改评论