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Improving the Critic Learning for Event-Based Nonlinear H-infinity Control Design 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2017, 卷号: 47, 期号: 10, 页码: 3417-3428
作者:  Wang, Ding;  He, Haibo;  Liu, Derong
浏览  |  Adobe PDF(1068Kb)  |  收藏  |  浏览/下载：418/110  |  提交时间：2018/03/03
H-infinity Control  Adaptive Systems  Adaptive/approximate Dynamic Programming  Critic Network  Event-based Design  Learning Criterion  Neural Control  
Developing nonlinear adaptive optimal regulators through an improved neural learning mechanism 期刊论文
SCIENCE CHINA-INFORMATION SCIENCES, 2017, 卷号: 60, 期号: 5
作者:  Wang, Ding;  Mu, Chaoxu
Adobe PDF(244Kb)  |  收藏  |  浏览/下载：154/67  |  提交时间：2017/09/12
Decentralized guaranteed cost control of interconnected systems with uncertainties: A learning-based optimal control strategy 期刊论文
NEUROCOMPUTING, 2016, 卷号: 214, 页码: 297-306
作者:  Wang, Ding;  Liu, Derong;  Mu, Chaoxu;  Ma, Hongwen
Adobe PDF(1113Kb)  |  收藏  |  浏览/下载：382/120  |  提交时间：2017/02/14
Adaptive Dynamic Programming  Decentralized Control  Guaranteed Cost Control  Interconnected Systems  Learning Control  Neural Networks  Optimal Control  Uncertain Plant  
Data-Based Adaptive Critic Designs for Nonlinear Robust Optimal Control With Uncertain Dynamics 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2016, 卷号: 46, 期号: 11, 页码: 1544-1555
作者:  Wang, Ding;  Liu, Derong;  Zhang, Qichao;  Zhao, Dongbin
浏览  |  Adobe PDF(1082Kb)  |  收藏  |  浏览/下载：441/197  |  提交时间：2017/02/14
Adaptive Critic Designs  Adaptive Dynamic Programming  Intelligent Control  Neural Networks  Policy Iteration  Robust Optimal Control  System Identification  Uncertain Nonlinear Systems  
Data-driven controller design for general MIMO nonlinear systems via virtual reference feedback tuning and neural networks 期刊论文
NEUROCOMPUTING, 2016, 卷号: 171, 页码: 815-825
作者:  Yan, Pengfei;  Liu, Derong;  Wang, Ding;  Ma, Hongwen
浏览  |  Adobe PDF(603Kb)  |  收藏  |  浏览/下载：522/118  |  提交时间：2016/01/18
Data-driven Control  Mimo Nonlinear Systems  Model Reference Control  Neural Networks  Virtual Reference Feedback Tuning