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Adaptive Critic Nonlinear Robust Control: A Survey 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2017, 卷号: 47, 期号: 10, 页码: 3429-3451
作者:  Wang, Ding;  He, Haibo;  Liu, Derong
Adobe PDF(1954Kb)  |  收藏  |  浏览/下载:439/148  |  提交时间:2018/03/03
Adaptive Critic Designs  Adaptive/approximate Dynamic Programming (Adp)  Boundedness  Convergence  Neural Networks  Optimal Control  Reinforcement Learning  Robust Control  Stability  
Online reinforcement learning for continuous-state systems 专著章节/文集论文
出自: Frontiers of Intelligent Control and Information Processing, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, Singapore, 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Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World Scientific, World 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作者:  Yuanheng Zhu;  Zhao DB(赵冬斌)
Adobe PDF(24150Kb)  |  收藏  |  浏览/下载:263/28  |  提交时间:2017/09/13
Intelligent Critic Control With Disturbance Attenuation for Affine Dynamics Including an Application to a Microgrid System 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2017, 卷号: 64, 期号: 6, 页码: 4935-4944
作者:  Wang, Ding;  He, Haibo;  Mu, Chaoxu;  Liu, Derong
Adobe PDF(728Kb)  |  收藏  |  浏览/下载:352/115  |  提交时间:2017/07/18
Adaptive/approximate Dynamic Programming  Adaptive Critic Control  Disturbance Attenuation  Intelligent Control  Neural Identification  Smart Microgrid  
Error Bounds of Adaptive Dynamic Programming Algorithms for Solving Undiscounted Optimal Control Problems 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 卷号: 26, 期号: 6, 页码: 1323-1334
作者:  Liu, Derong;  Li, Hongliang;  Wang, Ding
Adobe PDF(1114Kb)  |  收藏  |  浏览/下载:307/94  |  提交时间:2015/09/17
Adaptive Critic Designs  Adaptive Dynamic Programming (Adp)  Approximate Dynamic Programming  Neural Networks  Neurodynamic Programming  Nonlinear Systems  Optimal Control