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Nearly optimal stabilization of unknown continuous-time nonlinear systems: A new parallel control approach

文献类型：期刊论文

作者Lu, Jingwei1,2,3; Wang, Xingxia2,3; Wei, Qinglai2,3; Wang, Fei-Yue2,3
刊名NEUROCOMPUTING
出版日期2024-04-14
卷号578页码:12
关键词Adaptive dynamic programming (ADP) Integral reinforcement learning (IRL) Nearly optimal control Nonaffine nonlinearity Parallel control Unknown nonlinear systems
ISSN号0925-2312
DOI10.1016/j.neucom.2024.127421
通讯作者Lu, Jingwei(lujingwei@tsinghua.edu.cn)
英文摘要This paper develops a novel online nearly optimal control (ONOC) method for unknown continuous -time (CT) nonaffine nonlinear systems without recovering unknown systems. First, a dynamic control law is proposed for CT nonaffine nonlinear systems using parallel control. To achieve the proposed dynamic control law, an affine augmented system (AAS) is constructed according to the original system, and an augmented performance index (API) is constructed on the basis of the original performance index (OPI). Then, the stability relationship between the original system and the AAS is provided, and it is proven that, by selecting a suitable parameter in the API, optimal control of the AAS with the API is equivalent to near -optimal control of the original system with the OPI. Subsequently, based on the proposed dynamic control law, we extend integral reinforcement learning (IRL) to completely unknown CT nonaffine systems, and it is further proved that closed -loop signals are uniformly ultimately bounded (UUB) without the assumption that the input dynamics are bounded. Furthermore, the OPI can be set to an arbitrary positive -definite form, and the UUB bound for the state vector can be predetermined. Lastly, simulations are offered to exhibit the correctness of the developed ONOC method. Source code of this paper is available at: https://github.com/lujingweihh/Adaptive-dynamic-programmingalgorithms/tree/main/model_free_integral_reinforcement_learning.
WOS关键词OPTIMAL TRACKING CONTROL
资助项目Motion G, Inc. ; Collaborative Re-search Project for Fundamental Modeling and Parallel Drive-Control of Servo Drive Systems
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001205857900001
出版者ELSEVIER
资助机构Motion G, Inc. ; Collaborative Re-search Project for Fundamental Modeling and Parallel Drive-Control of Servo Drive Systems
源URL[http://ir.ia.ac.cn/handle/173211/58256]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_智能化团队
自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Lu, Jingwei
作者单位1.Tsinghua Univ, Dept Ind Engn, Beijing 100084, Peoples R China
2.Qingdao Acad Intelligent Ind, Qingdao 266114, Peoples R China
3.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		 Lu, Jingwei,Wang, Xingxia,Wei, Qinglai,et al. Nearly optimal stabilization of unknown continuous-time nonlinear systems: A new parallel control approach[J]. NEUROCOMPUTING,2024,578:12.
APA Lu, Jingwei,Wang, Xingxia,Wei, Qinglai,&Wang, Fei-Yue.(2024).Nearly optimal stabilization of unknown continuous-time nonlinear systems: A new parallel control approach.NEUROCOMPUTING,578,12.
MLA Lu, Jingwei,et al."Nearly optimal stabilization of unknown continuous-time nonlinear systems: A new parallel control approach".NEUROCOMPUTING 578(2024):12.

入库方式： OAI收割

来源：自动化研究所

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