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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：447/122  |  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  
Event-Driven Adaptive Robust Control of Nonlinear Systems With Uncertainties Through NDP Strategy 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2017, 卷号: 47, 期号: 7, 页码: 1358-1370
作者:  Wang, Ding;  Mu, Chaoxu;  He, Haibo;  Liu, Derong
Adobe PDF(1425Kb)  |  收藏  |  浏览/下载：392/143  |  提交时间：2017/09/12
Adaptive Dynamic Programming (Adp)  Adaptive Robust Control  Critic Neural Network  Event-driven Control  Neural Dynamic Programming (Ndp)  Uncertain Nonlinear Systems  
Error Bound Analysis of Q-Function for Discounted Optimal Control Problems With Policy Iteration 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2017, 卷号: 47, 期号: 7, 页码: 1207-1216
作者:  Yan, Pengfei;  Wang, Ding;  Li, Hongliang;  Liu, Derong
Adobe PDF(625Kb)  |  收藏  |  浏览/下载：357/86  |  提交时间：2017/09/12
Adaptive Dynamic Programming (Adp)  Error Analysis  Nonlinear Systems  Policy Iteration  Q-function  
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：437/123  |  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  
Event-based input-constrained nonlinear H infinity state feedback with adaptive critic and neural implementation 期刊论文
NEUROCOMPUTING, 2016, 卷号: 214, 期号: *, 页码: 848-856
作者:  Wang, Ding;  Mu, Chaoxu;  Zhang, Qichao;  Liu, Derong
Adobe PDF(1090Kb)  |  收藏  |  浏览/下载：363/137  |  提交时间：2017/02/14
Adaptive Critic Learning (Acl)  Adaptive Dynamic Programming (Adp)  Event-based Control  Hamilton-jacobi-isaacs (Hji) Equation  Input Constraints  Neural Networks  Nonlinear H-infinity Control  State Feedback  
Policy Gradient Adaptive Dynamic Programming for Data-Based Optimal Control 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2017, 卷号: 47, 期号: 10, 页码: 3341-3354
作者:  Luo, Biao;  Liu, Derong;  Wu, Huai-Ning;  Wang, Ding;  Lewis, Frank L.
浏览  |  Adobe PDF(3217Kb)  |  收藏  |  浏览/下载：604/213  |  提交时间：2016/11/09
Adaptive Control  Adaptive Dynamic Programming (Adp)  Data-based  Off-policy Learning  Optimal Control  Policy Gradient  
Data-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2016, 卷号: 27, 期号: 2, 页码: 444-458
作者:  Wei, Qinglai;  Song, Ruizhuo;  Yan, Pengfei
浏览  |  Adobe PDF(2204Kb)  |  收藏  |  浏览/下载：431/141  |  提交时间：2016/06/14
Adaptive Critic Designs  Adaptive Dynamic Programming (Adp)  Approximate Dynamic Programming  Neurodynamic Programming  Nonlinear Systems  Optimal Control  Recurrent Neural Network (Rnn)  Reinforcement Learning  
Value Iteration Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2016, 卷号: 46, 期号: 3, 页码: 840-853
作者:  Wei, Qinglai;  Liu, Derong;  Lin, Hanquan;  Derong Liu
Adobe PDF(2015Kb)  |  收藏  |  浏览/下载：389/170  |  提交时间：2016/06/14
Adaptive Critic Designs  Adaptive Dynamic Programming (Adp)  Approximate Dynamic Programming  Neural Networks  Neuro-dynamic Programming  Optimal Control  Reinforcement Learning  Value Iteration  
Online approximate solution of HJI equation for unknown constrained-input nonlinear continuous-time systems 期刊论文
INFORMATION SCIENCES, 2016, 卷号: 328, 页码: 435-454
作者:  Yang, Xiong;  Liu, Derong;  Ma, Hongwen;  Xu, Yancai
浏览  |  Adobe PDF(833Kb)  |  收藏  |  浏览/下载：434/123  |  提交时间：2016/01/18
Adaptive Dynamic Programming  Hamilton-jacobi-isaacs Equation  Input Constraint  Neural Network  Optimal Control  Reinforcement Learning