Neuro-Optimal Trajectory Tracking With Value Iteration of Discrete-Time Nonlinear Dynamics

doi:10.1109/TNNLS.2021.3123444

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 > 先进机器人控制团队

	Neuro-Optimal Trajectory Tracking With Value Iteration of Discrete-Time Nonlinear Dynamics
	Wang, Ding 2,3; Ha, Mingming 1; Cheng, Long 4,5
刊名	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
	2021-11-08
页码	12
关键词	Trajectory Heuristic algorithms Convergence Trajectory tracking Stability criteria Optimal control Dynamic programming Adaptive critic design discrete-time nonlinear plants neuro-optimal trajectory tracking uniformly ultimately bounded stability value iteration
ISSN号	2162-237X
DOI	10.1109/TNNLS.2021.3123444
通讯作者	Wang, Ding(dingwang@bjut.edu.cn)
英文摘要	In this article, a novel neuro-optimal tracking control approach is developed toward discrete-time nonlinear systems. By constructing a new augmented plant, the optimal trajectory tracking design is transformed into an optimal regulation problem. For discrete-time nonlinear dynamics, the steady control input corresponding to the reference trajectory is given. Then, the value-iteration-based tracking control algorithm is provided and the convergence of the value function sequence is established. Therein, the approximation error between the iterative value function and the optimal cost is estimated. The uniformly ultimately bounded stability of the closed-loop system is also discussed in detail. Moreover, the iterative heuristic dynamic programming (HDP) algorithm is implemented by involving the critic and action components, where some new updating rules of the action network are provided. Finally, two examples are used to demonstrate the optimality of the present controller as well as the effectiveness of the proposed method.
资助项目	Beijing Natural Science Foundation[JQ19013] ; National Natural Science Foundation of China[61773373] ; National Natural Science Foundation of China[61873268] ; National Natural Science Foundation of China[62025307] ; National Natural Science Foundation of China[61890930-5] ; National Natural Science Foundation of China[62021003] ; National Key Research and Development Project[2018YFC1900800-5]
WOS关键词	ADAPTIVE-CONTROL ; LINEAR-SYSTEMS ; STABILITY
WOS研究方向	Computer Science ; Engineering
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000733510800001
资助机构	Beijing Natural Science Foundation ; National Natural Science Foundation of China ; National Key Research and Development Project
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46938]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队
通讯作者	Wang, Ding
作者单位	1.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China 2.Beijing Univ Technol, Beijing Inst Artificial Intelligence, Beijing 100124, Peoples R China 3.Beijing Univ Technol, Fac Informat Technol, Beijing Key Lab Computat Intelligence & Intellige, Beijing Lab Smart Environm Protect, Beijing 100124, Peoples R China 4.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wang, Ding,Ha, Mingming,Cheng, Long. Neuro-Optimal Trajectory Tracking With Value Iteration of Discrete-Time Nonlinear Dynamics[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021:12.
APA	Wang, Ding,Ha, Mingming,&Cheng, Long.(2021).Neuro-Optimal Trajectory Tracking With Value Iteration of Discrete-Time Nonlinear Dynamics.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,12.
MLA	Wang, Ding,et al."Neuro-Optimal Trajectory Tracking With Value Iteration of Discrete-Time Nonlinear Dynamics".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021):12.