中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 多模态人工智能系统全国重点实验室
  5. 智能化团队
Model-Free Optimal Tracking Control via Critic-Only Q-Learning

文献类型：期刊论文

作者Luo, Biao1; Liu, Derong2; Huang, Tingwen3; Wang, Ding1; Luo,Biao
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2016-10-01
卷号27期号:10页码:2134-2144
关键词Critic-only Q-learning (Coql) Model-free Nonaffine Nonlinear Systems Optimal Tracking Control
DOI10.1109/TNNLS.2016.2585520
文献子类Article
英文摘要Model-free control is an important and promising topic in control fields, which has attracted extensive attention in the past few years. In this paper, we aim to solve the model-free optimal tracking control problem of nonaffine non-linear discrete-time systems. A critic-only Q-learning (CoQL) method is developed, which learns the optimal tracking control from real system data, and thus avoids solving the tracking Hamilton-Jacobi-Bellman equation. First, the Q-learning algorithm is proposed based on the augmented system, and its convergence is established. Using only one neural network for approximating the Q-function, the CoQL method is developed to implement the Q-learning algorithm. Furthermore, the convergence of the CoQL method is proved with the consideration of neural network approximation error. With the convergent Q-function obtained from the CoQL method, the adaptive optimal tracking control is designed based on the gradient descent scheme. Finally, the effectiveness of the developed CoQL method is demonstrated through simulation studies. The developed CoQL method learns with off-policy data and implements with a critic-only structure, thus it is easy to realize and overcome the inadequate exploration problem.
WOS关键词TIME NONLINEAR-SYSTEMS ; H-INFINITY CONTROL ; ADAPTIVE OPTIMAL-CONTROL ; SPATIALLY DISTRIBUTED PROCESSES ; LINEAR-SYSTEMS ; CONTROL DESIGN ; UNKNOWN DYNAMICS ; CONTROL SCHEME ; ATTITUDE TRACKING ; POLICY ITERATION
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000384644000012
资助机构National Natural Science Foundation of China(61233001 ; State Key Laboratory of Management and Control for Complex Systems ; National Priorities Research Program through the Qatar National Research Fund (a member of Qatar Foundation)(NPRP 7-1482-1-278) ; 61273140 ; 61304086 ; 61374105 ; 61503377 ; 61533017 ; U1501251)
源URL[http://ir.ia.ac.cn/handle/173211/12301]  
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
通讯作者Luo,Biao
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
2.Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
3.Texas A&M Univ Qatar, Doha 23874, Qatar
推荐引用方式
GB/T 7714		 Luo, Biao,Liu, Derong,Huang, Tingwen,et al. Model-Free Optimal Tracking Control via Critic-Only Q-Learning[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2016,27(10):2134-2144.
APA Luo, Biao,Liu, Derong,Huang, Tingwen,Wang, Ding,&Luo,Biao.(2016).Model-Free Optimal Tracking Control via Critic-Only Q-Learning.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,27(10),2134-2144.
MLA Luo, Biao,et al."Model-Free Optimal Tracking Control via Critic-Only Q-Learning".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 27.10(2016):2134-2144.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

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。