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Adaptive dynamic programming-based stabilization of nonlinear systems with unknown actuator saturation 期刊论文
NONLINEAR DYNAMICS, 2018, 卷号: 93, 期号: 4, 页码: 2089-2103
作者:  Zhao, Bo;  Jia, Lihao;  Xia, Hongbing;  Li, Yuanchun
浏览  |  Adobe PDF(1069Kb)  |  收藏  |  浏览/下载：366/130  |  提交时间：2018/10/10
Adaptive Dynamic Programming  Unknown Actuator Saturation  Continuous-time Nonlinear Systems  Stabilizing Control  Neural Networks  
Adaptive Constrained Optimal Control Design for Data-Based Nonlinear Discrete-Time Systems With Critic-Only Structure 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 6, 页码: 2099-2111
作者:  Luo, Biao;  Liu, Derong;  Wu, Huai-Ning
浏览  |  Adobe PDF(1045Kb)  |  收藏  |  浏览/下载：368/113  |  提交时间：2018/10/10
Adaptive Control  Adaptive Dynamic Programming  Constraints  Critic-only  Data-based  Optimal Control  Q-learning  
Connectivity preserved nonlinear time-delayed multiagent systems using neural networks and event-based mechanism 期刊论文
NEURAL COMPUTING & APPLICATIONS, 2018, 卷号: 29, 期号: 9, 页码: 361-369
作者:  Ma, Hongwen;  Wang, Ding;  Ding Wang
浏览  |  Adobe PDF(1038Kb)  |  收藏  |  浏览/下载：397/104  |  提交时间：2017/05/03
Connectivity Preservation  Event-based Control  Multiagent Systems  Neural Networks  
Data-Based Optimal Control for Weakly Coupled Nonlinear Systems Using Policy Iteration 期刊论文
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2018, 卷号: 48, 期号: 4, 页码: 511-521
作者:  Li, Chao;  Liu, Derong;  Wang, Ding
浏览  |  Adobe PDF(892Kb)  |  收藏  |  浏览/下载：859/570  |  提交时间：2017/05/03
Adaptive Dynamic Programming (Adp)  Neural Networks (Nns)  Optimal Control  Policy Iteration (Pi)  Unknown Dynamics  Weakly Coupled Systems  
Policy Iteration for H infinity Optimal Control of Polynomial Nonlinear Systems via Sum of Squares Programming 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2018, 卷号: 48, 期号: 2, 页码: 500-509
作者:  Zhu, Yuanheng;  Zhao, Dongbin;  Yang, Xiong;  Zhang, Qichao
Adobe PDF(892Kb)  |  收藏  |  浏览/下载：274/33  |  提交时间：2018/10/10
Adaptive Dynamic Programming (Adp)  h Infinity Optimal Control  Policy Iteration (Pi)  Polynomial Nonlinear Systems  Sum Of Squares (Sos)  
A pdf-Free Change Detection Test Based on Density Difference Estimation 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 2, 页码: 324-334
作者:  Bu, Li;  Alippi, Cesare;  Zhao, Dongbin
浏览  |  Adobe PDF(2468Kb)  |  收藏  |  浏览/下载：356/104  |  提交时间：2017/05/04
Concept Drift  Least Squares Density-difference (Lsdd)-based Method  Probability Density Function (Pdf)-free  Three-level Threshold Mechanism  
Event-Based Robust Control for Uncertain Nonlinear Systems Using Adaptive Dynamic Programming 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 1, 页码: 37-50
作者:  Zhang, Qichao;  Zhao, Dongbin;  Wang, Ding
浏览  |  Adobe PDF(2233Kb)  |  收藏  |  浏览/下载：507/238  |  提交时间：2017/05/04
Adaptive Dynamic Programming (Adp)  Event-based Control  Neural Network (Nn)  Robust Control  Unmatched Uncertainties  
A novel neural optimal control framework with nonlinear dynamics: Closed-loop stability and simulation verification 期刊论文
NEUROCOMPUTING, 2017, 卷号: 266, 页码: 353-360
作者:  Wang, Ding;  Mu, Chaoxu
Adobe PDF(1503Kb)  |  收藏  |  浏览/下载：292/61  |  提交时间：2018/03/03
Adaptive Dynamic Programming  Adaptive System  Learning Control  Neural Network  Optimal Regulator  Stability  
Optimal control for discrete-time systems with actuator saturation 期刊论文
OPTIMAL CONTROL APPLICATIONS & METHODS, 2017, 卷号: 38, 期号: 6, 页码: 1071-1080
作者:  Lin, Qiao;  Wei, Qinglai;  Zhao, Bo
浏览  |  Adobe PDF(348Kb)  |  收藏  |  浏览/下载：509/253  |  提交时间：2017/05/04
Approximate Dynamic Programming  Discrete Time  Generalized Policy Iteration  Optimal Control  Saturating Actuators  
Adaptive tracking control for a class of continuous-time uncertain nonlinear systems using the approximate solution of HJB equation 期刊论文
NEUROCOMPUTING, 2017, 卷号: 260, 页码: 432-442
作者:  Mu, Chaoxu;  Sun, Changyin;  Wang, Ding;  Song, Aiguo
Adobe PDF(1555Kb)  |  收藏  |  浏览/下载：403/155  |  提交时间：2017/09/12
Adaptive Tracking Control  Hamilton-jacobi-bellman (Hjb) Equation  Adaptive Dynamic Programming (Adp)  Neural Networks  Uncertainties