Model-Free Optimal Tracking Control via Critic-Only Q-Learning
Luo, Biao1; Liu, Derong2; Huang, Tingwen3; Wang, Ding1; Luo,Biao
2016-10-01
发表期刊IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷号27期号:10页码:2134-2144
文章类型Article
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.
其他摘要
关键词Critic-only Q-learning (Coql) Model-free Nonaffine Nonlinear Systems Optimal Tracking Control
WOS标题词Science & Technology ; Technology
DOI10.1109/TNNLS.2016.2585520
关键词[WOS]TIME NONLINEAR-SYSTEMS ; H-INFINITY CONTROL ; ADAPTIVE OPTIMAL-CONTROL ; SPATIALLY DISTRIBUTED PROCESSES ; LINEAR-SYSTEMS ; CONTROL DESIGN ; UNKNOWN DYNAMICS ; CONTROL SCHEME ; ATTITUDE TRACKING ; POLICY ITERATION
收录类别SCI
语种英语
项目资助者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)
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:000384644000012
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/12301
专题复杂系统管理与控制国家重点实验室_平行控制
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.
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