作者其他论文
文献详情
Reinforcement Learning Output Feedback NN Control Using Deterministic Learning Technique
文献类型:期刊
作者:Xu, Bin[1]  Yang, Chenguang[2]  Shi, Zhongke[3]  
机构:[1]School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
[2]School of Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, United States |School of Automation, Beijing, Institute of Technology, Beijing, 100086, China
[3]School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
通讯作者:Xu, B (reprint author), Northwestern Polytech Univ, Sch Automat, Xian 710072, Peoples R China.
年:2014
期刊名称:IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS影响因子和分区
卷:25
期:3
页码范围:635-641
增刊:正刊
学科:计算机科学
收录情况:SCI(E)(WOS:000331985500015)  EI(20141117440325)  
所属部门:自动化学院
重要成果类型:ESI高被引
被引频次:114
人气指数:7741
浏览次数:7624
基金:National Science Foundation of China [61304098, 61134004]; Foundation of Key Laboratory of Autonomous Systems and Networked Control (Ministry of Education, China) [2012A04]; EU [PIIFR-GA-2010-910078-H2R]; NWPU [JC20120236]
关键词:Approximate dynamic programming; discrete-time system; output feedback control; pure-feedback system; radial basis function neural network (RBF NN)
摘要:
In this brief, a novel adaptive-critic-based neural network (NN) controller is investigated for nonlinear pure-feedback systems. The controller design is based on the transformed predictor form, and the actor-critic NN control architecture includes two NNs, whereas the critic NN is used to approximate the strategic utility function, and the action NN is employed to minimize both the strategic utility function and the tracking error. A deterministic learning technique has been employed to guarant ...More
0
评论(0 条评论)
登录