Adaptive Optimal Control of Highly Dissipative Nonlinear Spatially Distributed Processes With Neuro-Dynamic Programming

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	Adaptive Optimal Control of Highly Dissipative Nonlinear Spatially Distributed Processes With Neuro-Dynamic Programming
	Luo, Biao1,2 ; Wu, Huai-Ning 1; Li, Han-Xiong 3
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2015-04-01
卷号	26 期号:4 页码:684-696
关键词	Adaptive optimal control empirical eigenfunction (EEF) highly dissipative partial differential equations (PDEs) neuro-dynamic programming (NDP) spatially distributed processes (SDPs)
英文摘要	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.
WOS标题词	Science & Technology ; Technology
类目[WOS]	Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
研究领域[WOS]	Computer Science ; Engineering
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记录号	WOS:000351835900004
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/10738]
专题	复杂系统管理与控制国家重点实验室_平行控制
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
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.